Sample Application of vision model and global tiling:

import torch 

from transformers import AutoProcessor, AutoModel 

import requests 

from PIL import Image 

import io 

import numpy as np 

from sklearn.neighbors import NearestNeighbors 

# ------------------------------------------------------------ 

# 1. Load Prithvi EO 2.0 model + processor 

# ------------------------------------------------------------ 

model_name = "ibm-nasa-geospatial/Prithvi-EO-2.0-300M" 

 

processor = AutoProcessor.from_pretrained(model_name) 

model = AutoModel.from_pretrained(model_name) 

model.eval() 

 

# ------------------------------------------------------------ 

# 2. Load drone image, say from SAS URL 

# ------------------------------------------------------------ 

url = "https://sadronevideo.blob.core.windows.net/input/interesting/what-location.jpg?sp=r&st=2026-09-13T01:22:02Z&se=2026-09-13T09:37:02Z&spr=https&sv=2026-02-06&sr=b&sig=9Ab0REdBAyuLT5lsOizuRLd8ijPtqle8XtOvw%2FjjDKQ%3D" 

 

response = requests.get(url) 

image = Image.open(io.BytesIO(response.content)).convert("RGB") 

 

# ------------------------------------------------------------ 

# 3. Preprocess + embed using Prithvi EO 2.0 

# ------------------------------------------------------------ 

inputs = processor(images=image, return_tensors="pt") 

 

with torch.no_grad(): 

    outputs = model(**inputs) 

    # Prithvi returns last_hidden_state; we pool it to get a single vector 

    embedding = outputs.last_hidden_state.mean(dim=1).squeeze().cpu().numpy() 

 

print("Embedding shape:", embedding.shape) 

 

# ------------------------------------------------------------ 

# 4. Build a tiny reference corpus  

# Each entry: (embedding_vector, (lat, lon)) 

# ------------------------------------------------------------ 

 

# Example reference embeddings  

reference_embeddings = np.random.rand(5, embedding.shape[0]) reference_locations = [ 

    (37.769939, -122.387722), # San Francisco 

    (47.608494, -122.339175), # Seattle 

    (40.706347, -74.010397), # New York 

    (25.758758, -80.191192), # Miami 

    (42.371839, -71.117986), # Cambridge  

 

] 

REFERENCE_DIR = "./reference_tiles" 

 

tile_files = [ 

    ("Cambridge.jpg", (42.371839, -71.117986)), 

    ("Miami.jpg", (25.758758, -80.191192)), 

    ("NewYork.jpg", (40.706347, -74.010397)), 

    ("SanFrancisco.jpg", (37.769939, -122.387722)), 

    ("Seattle.jpg", (47.608494, -122.339175)), 

] 

 

image_paths = [os.path.join(REFERENCE_DIR, f[0]) for f in tile_files] 

gps_coords = [f[1] for f in tile_files] 

embeddings = [] 

images = [] 

 

for path in image_paths: 

    img = Image.open(path).convert("RGB") 

    images.append(img) 

 

    inputs = processor(images=img, return_tensors="pt") 

 

    with torch.no_grad(): 

        outputs = model(**inputs) 

        emb = outputs.last_hidden_state.mean(dim=1).squeeze().cpu().numpy() 

 

    embeddings.append(emb) 

 

embeddings = np.array(embeddings, dtype=np.float32) 

images = np.array(images, dtype=object) 

gps_coords = np.array(gps_coords, dtype=np.float32) 

 

np.save("reference_embeddings.npy", embeddings) 

np.save("reference_images.npy", images) 

np.save("reference_gps.npy", gps_coords) 

 

reference_embeddings = np.load("earth_tile_embeddings.npy") 

reference_locations = np.load("earth_tile_locations.npy") 

 

 

# ------------------------------------------------------------ 

# 5. Fit nearest-neighbor search 

# ------------------------------------------------------------ 

nn = NearestNeighbors(n_neighbors=1, metric="cosine") 

nn.fit(reference_embeddings) 

 

dist, idx = nn.kneighbors([embedding]) 

best_index = idx[0][0] 

best_distance = dist[0][0] 

 

estimated_location = reference_locations[best_index] 

 

print("\nEstimated GPS coordinates:", estimated_location) 

print("Cosine distance:", best_distance)

## Result:

# Nearest match GPS: [ 42.371839 -71.117986 ]

# Cosine distance: 0.82509133

 Passkeys represent one of the most significant advances in authentication architecture during the transition away from passwords because they replace reusable shared secrets with asymmetric cryptography. A passkey registered for a particular relying party cannot simply be harvested from a fraudulent login page and replayed against the legitimate service. This capability substantially reduces traditional phishing risks and strengthens identity assurance for consumer and enterprise systems alike. However, recent attacks demonstrate that strong authentication does not automatically guarantee secure identity enrollment. The most important lesson for software engineers, cloud architects, security researchers, and identity platform designers is that the security of a credential is ultimately constrained by the security of the processes that create, replace, recover, and manage that credential. 

The emerging threat model is not based on defeating passkey cryptography. Instead, attackers exploit weaknesses in human trust, enrollment workflows, and account recovery procedures. In a typical attack scenario, a user is deceived into believing they are participating in a legitimate credential enrollment process. A convincing website is created using terminology associated with authentication modernization and security deployment initiatives. Attackers often reinforce the deception through voice-based social engineering, presenting themselves as internal support personnel or identity administrators. The victim voluntarily enters credentials into the fraudulent system, enabling the attacker to authenticate to the victim's real account. Once sufficient account control has been obtained, the attacker initiates a legitimate passkey enrollment process and registers a new credential on hardware under the attacker's control. The resulting credential is valid because it was created through the system's authorized enrollment pathway rather than through cryptographic compromise. 

This distinction is critical from a computer science perspective because it reveals a separation between authentication security and enrollment security. Authentication protocols based on public key cryptography can remain mathematically sound while the surrounding identity lifecycle remains vulnerable. The attacker does not need to extract an existing private key, break FIDO-based protocols, or subvert origin binding protections. Instead, the attacker leverages a weaker trust path that permits enrollment of a new credential. In formal security terms, the enrollment process becomes the weakest link in the trust graph. If a weaker mechanism can authorize creation of a stronger mechanism, then overall system security is effectively bounded by the weaker mechanism. 

The problem is highly relevant to large-scale cloud systems such as consumer e-commerce platforms and cloud service providers. Consider an Amazon.com customer account or an AWS account protected by passkeys. If an attacker successfully obtains enough control over the account through phishing, credential theft, session hijacking, support-assisted recovery, or other identity manipulation techniques, the attacker may be able to register an additional authenticator through legitimate enrollment workflows. The cryptographic properties of the existing passkey remain intact, yet the attacker acquires persistent access because the platform now recognizes an attacker-controlled credential as valid. From an architectural standpoint, the system correctly validates cryptographic assertions while simultaneously failing to verify that the individual performing enrollment is truly the legitimate account owner. 

Traditional software systems often treat possession of an authenticated session as sufficient authority for sensitive identity-management operations. This assumption becomes problematic when enrollment, recovery, authenticator replacement, device registration, privilege escalation, and account restoration procedures inherit trust solely from account access. A security architecture that provides strong protection at login but weaker controls during credential issuance introduces a privilege inversion. The attacker discovers that compromising the enrollment workflow is easier than compromising the credential itself and therefore redirects effort toward the weaker target. 

A more resilient model requires binding authentication credentials to dedicated biometric hardware and enforcing strong physical verification during both enrollment and use. In such systems, private keys remain inside purpose-built secure hardware and are not intended to be exported, copied, or silently migrated across devices. Authentication requests require biometric verification directly on the hardware authenticator, ensuring that possession of an account alone is insufficient for sensitive operations. Additionally, physical proximity mechanisms can require the authenticator to be near the endpoint requesting access, creating stronger assurance that the authenticated individual is physically present during enrollment or credential-management activities. 

From a systems engineering viewpoint, dedicated biometric hardware shifts the trust model from simple credential possession toward verification of the person, device, endpoint, and service simultaneously. The enrollment process becomes dependent on multiple independent factors rather than on authenticated account access alone. Because the credential remains resident within secure hardware, attackers cannot easily duplicate the private key or synchronize it across unauthorized devices. Biometric activation ensures that the credential cannot be exercised merely by stealing a session or convincing a user to approve a remote workflow. Physical proximity requirements further reduce the risk that a victim can unknowingly serve as a remote authorization oracle for an attacker operating elsewhere. 

This approach also addresses a common misconception in passwordless security initiatives. Organizations frequently focus on login events while underestimating the importance of credential lifecycle management. Yet every trust-altering operation represents a security boundary. Initial enrollment, authenticator replacement, account recovery, device association, credential revocation, delegated administration, help desk assisted restoration, and privilege elevation all require equivalent levels of assurance if the overall identity system is to remain secure. A secure login process cannot compensate for an insecure enrollment process, just as a secure cryptographic protocol cannot compensate for weak key generation procedures. 

For software engineering teams building authentication systems, the broader lesson is that identity assurance must be evaluated end-to-end rather than component-by-component. Security reviews should analyze not only how authentication assertions are validated but also how credentials are created, when new authenticators can be added, how recovery is performed, which entities approve enrollment actions, and what evidence is required to establish user presence. Threat models should explicitly consider social engineering campaigns that target credential enrollment rather than credential usage. Formal security properties such as origin binding, challenge-response authentication, and cryptographic non-repudiation must be complemented by procedural controls enforcing verified user presence and device legitimacy. 

The future of high-assurance identity systems will likely be determined less by the strength of authentication algorithms and more by the integrity of the entire identity lifecycle. Passkeys remain a substantial improvement over passwords and dramatically reduce many traditional attack vectors. Nevertheless, cryptographic strength alone does not eliminate the possibility of account takeover when attackers can manipulate users and exploit enrollment workflows. For high-value environments such as cloud infrastructure, enterprise administration, financial systems, and large-scale consumer platforms including Amazon.com and AWS, the strongest security posture emerges when credential enrollment, credential use, credential recovery, and credential replacement are governed by the same rigorous assurance standard. Only then can organizations ensure that the entity creating a credential is the same trusted individual ultimately granted access to protected resources. 

 

 Improving disambiguation between Camera-based and AI-generated/AI-enhanced drone footages:

We introduce the following three perpectives:

                 ┌──► Spatial Co-occurrence Matrix (Pixel-to-neighbor joint distribution)

                 │

[Input Image] ───┼──► Discrete Cosine Transform (DCT) (Fourier-domain frequency profiling)

                 │

                 └──► Local Variance Descriptors (Chrominance-to-luminance edge behavior)

Let’s unpack these and then fold them into a more robust routine that goes beyond our current residual_corr / residual_energy / inlier_ratio / reproj_error thresholds.

First, spatial co occurrence matrices. What, say Pangram, is really doing is measuring how often a pixel of intensity i sits next to a pixel of intensity j in a local window, and then looking at the joint distribution. Natural sensor noise tends to produce smooth, non gridlike co occurrence patterns; synthetic images often show overly uniform transitions or periodic structures from upsampling kernels. In our current code, residual_corr is already a kind of “sensor pattern” measure, but it’s global and correlation based. We can deepen this by explicitly computing gray level co occurrence matrices (GLCM) over patches and extracting texture statistics (contrast, homogeneity, energy, entropy) and then aggregating them across frames. The key is not to threshold each statistic individually, but to treat them as a vector and look at how that vector differs between camera and synthetic clips.

Second, the Discrete Cosine Transform. Pangram’s “spectral scars” language is pointing at the fact that diffusion and GAN pipelines leave characteristic energy spikes in high frequency bands. Our residual_energy is a spatial domain measure; we can complement it with a frequency domain profile. For each frame (or a subset), compute a 2D DCT, isolate high frequency coefficients, and summarize their energy distribution—mean, variance, kurtosis, maybe a few radial bands. Again, the point is not “high frequency = synthetic”; it’s that the shape of the high frequency energy distribution differs between physical sensor noise and algorithmic upsampling/denoising.

Third, local variance descriptors across chrominance and luminance. Natural lenses and sensors couple RGB channels in specific ways; generative pipelines often treat them more independently. We can approximate this by computing local variance in Y (luminance) and in Cb/Cr (chrominance) and then looking at how edges and textures behave across channels. Misaligned edge variance profiles—edges strong in luminance but oddly weak or misaligned in chrominance—are a tell.

Now, the grain and geometry thresholds we’ve set are deliberately conservative, and that’s good. But they’re still scalar thresholds on marginal distributions. Real footage can have low grain (good lighting, strong denoising), and synthetic footage can have rigid geometry and persistent tracks (good generator, or hybrid footage). So instead of trying to “fix” those thresholds, I’d treat our current features as part of a larger feature vector and move to a multi feature decision rule.

Concretely, I’d do something like this:

1. Keep our existing features: residual_corr, residual_energy, inlier_ratio, reproj_error, track_survival, motion_jerk, plus provenance flags (generator_tag, c2pa_ai_manifest, camera_metadata, telemetry).

2. Add three new feature families:

a. GLCM texture stats over residuals and raw grayscale:

i. contrast, homogeneity, energy, entropy, correlation.

b. DCT high frequency profile:

i. mean energy in high frequency band, variance, kurtosis, maybe a few band ratios.

c. chrominance luminance variance coupling:

i. correlation between local variance in Y and in Cb/Cr along edges.

3. Normalize these features per clip using robust statistics (medians, percentiles) across bursts, not single frames. We’re already sampling bursts; extend that to these new features.

4. Instead of hard thresholds, use either:

a. a simple classifier trained on a small labeled set (logistic regression, random forest), or

b. a one class anomaly detector trained on camera footage only (one class SVM, isolation forest), where “synthetic/enhanced” is “far from the camera manifold”.

We don’t need a giant dataset to get value here; even a few well curated camera clips and synthetic clips will give us a sense of how these features separate which we already have started.

 Passkeys represent one of the most significant advances in authentication architecture during the transition away from passwords because they replace reusable shared secrets with asymmetric cryptography. A passkey registered for a particular relying party cannot simply be harvested from a fraudulent login page and replayed against the legitimate service. This capability substantially reduces traditional phishing risks and strengthens identity assurance for consumer and enterprise systems alike. However, recent attacks demonstrate that strong authentication does not automatically guarantee secure identity enrollment. The most important lesson for software engineers, cloud architects, security researchers, and identity platform designers is that the security of a credential is ultimately constrained by the security of the processes that create, replace, recover, and manage that credential.

The emerging threat model is not based on defeating passkey cryptography. Instead, attackers exploit weaknesses in human trust, enrollment workflows, and account recovery procedures. In a typical attack scenario, a user is deceived into believing they are participating in a legitimate credential enrollment process. A convincing website is created using terminology associated with authentication modernization and security deployment initiatives. Attackers often reinforce the deception through voice-based social engineering, presenting themselves as internal support personnel or identity administrators. The victim voluntarily enters credentials into the fraudulent system, enabling the attacker to authenticate to the victim's real account. Once sufficient account control has been obtained, the attacker initiates a legitimate passkey enrollment process and registers a new credential on hardware under the attacker's control. The resulting credential is valid because it was created through the system's authorized enrollment pathway rather than through cryptographic compromise.

This distinction is critical from a computer science perspective because it reveals a separation between authentication security and enrollment security. Authentication protocols based on public key cryptography can remain mathematically sound while the surrounding identity lifecycle remains vulnerable. The attacker does not need to extract an existing private key, break FIDO-based protocols, or subvert origin binding protections. Instead, the attacker leverages a weaker trust path that permits enrollment of a new credential. In formal security terms, the enrollment process becomes the weakest link in the trust graph. If a weaker mechanism can authorize creation of a stronger mechanism, then overall system security is effectively bounded by the weaker mechanism.

The problem is highly relevant to large-scale cloud systems such as consumer e-commerce platforms and cloud service providers. Consider an Amazon.com customer account or an AWS account protected by passkeys. If an attacker successfully obtains enough control over the account through phishing, credential theft, session hijacking, support-assisted recovery, or other identity manipulation techniques, the attacker may be able to register an additional authenticator through legitimate enrollment workflows. The cryptographic properties of the existing passkey remain intact, yet the attacker acquires persistent access because the platform now recognizes an attacker-controlled credential as valid. From an architectural standpoint, the system correctly validates cryptographic assertions while simultaneously failing to verify that the individual performing enrollment is truly the legitimate account owner.

Traditional software systems often treat possession of an authenticated session as sufficient authority for sensitive identity-management operations. This assumption becomes problematic when enrollment, recovery, authenticator replacement, device registration, privilege escalation, and account restoration procedures inherit trust solely from account access. A security architecture that provides strong protection at login but weaker controls during credential issuance introduces a privilege inversion. The attacker discovers that compromising the enrollment workflow is easier than compromising the credential itself and therefore redirects effort toward the weaker target.

A more resilient model requires binding authentication credentials to dedicated biometric hardware and enforcing strong physical verification during both enrollment and use. In such systems, private keys remain inside purpose-built secure hardware and are not intended to be exported, copied, or silently migrated across devices. Authentication requests require biometric verification directly on the hardware authenticator, ensuring that possession of an account alone is insufficient for sensitive operations. Additionally, physical proximity mechanisms can require the authenticator to be near the endpoint requesting access, creating stronger assurance that the authenticated individual is physically present during enrollment or credential-management activities.

From a systems engineering viewpoint, dedicated biometric hardware shifts the trust model from simple credential possession toward verification of the person, device, endpoint, and service simultaneously. The enrollment process becomes dependent on multiple independent factors rather than on authenticated account access alone. Because the credential remains resident within secure hardware, attackers cannot easily duplicate the private key or synchronize it across unauthorized devices. Biometric activation ensures that the credential cannot be exercised merely by stealing a session or convincing a user to approve a remote workflow. Physical proximity requirements further reduce the risk that a victim can unknowingly serve as a remote authorization oracle for an attacker operating elsewhere.

This approach also addresses a common misconception in passwordless security initiatives. Organizations frequently focus on login events while underestimating the importance of credential lifecycle management. Yet every trust-altering operation represents a security boundary. Initial enrollment, authenticator replacement, account recovery, device association, credential revocation, delegated administration, help desk assisted restoration, and privilege elevation all require equivalent levels of assurance if the overall identity system is to remain secure. A secure login process cannot compensate for an insecure enrollment process, just as a secure cryptographic protocol cannot compensate for weak key generation procedures.

For software engineering teams building authentication systems, the broader lesson is that identity assurance must be evaluated end-to-end rather than component-by-component. Security reviews should analyze not only how authentication assertions are validated but also how credentials are created, when new authenticators can be added, how recovery is performed, which entities approve enrollment actions, and what evidence is required to establish user presence. Threat models should explicitly consider social engineering campaigns that target credential enrollment rather than credential usage. Formal security properties such as origin binding, challenge-response authentication, and cryptographic non-repudiation must be complemented by procedural controls enforcing verified user presence and device legitimacy.

The future of high-assurance identity systems will likely be determined less by the strength of authentication algorithms and more by the integrity of the entire identity lifecycle. Passkeys remain a substantial improvement over passwords and dramatically reduce many traditional attack vectors. Nevertheless, cryptographic strength alone does not eliminate the possibility of account takeover when attackers can manipulate users and exploit enrollment workflows. For high-value environments such as cloud infrastructure, enterprise administration, financial systems, and large-scale consumer platforms including Amazon.com and AWS, the strongest security posture emerges when credential enrollment, credential use, credential recovery, and credential replacement are governed by the same rigorous assurance standard. Only then can organizations ensure that the entity creating a credential is the same trusted individual ultimately granted access to protected resources.

 # https://claude.ai/public/artifacts/ab215f0c-c8d7-4974-a90b-288fe4bb43be

#!/usr/bin/env python3

"""

Select the four corner frames of a drone survey area from aerial video.

Two modes:

  telemetry -- if a DJI-style .SRT sidecar with [latitude]/[longitude] is present,

               the flight track comes straight from GPS (true north-up).

  visual -- otherwise, estimate the track by accumulating frame-to-frame

               similarity transforms (rough visual odometry on the ground plane).

In both modes the track is reduced to its minimum-area rotated rectangle; the

frame nearest each rectangle corner is emitted, ordered bottom-left then

clockwise (BL -> TL -> TR -> BR).

Usage:

    python survey_corners.py VIDEO [-o OUTDIR] [--srt FILE] [--fps 2] [--width 640]

"""

import argparse

import math

import os

import re

import sys

import cv2

import numpy as np

SRT_LAT = re.compile(r"\[latitude\s*:\s*([-\d.]+)\]")

SRT_LON = re.compile(r"\[long(?:i)?tude\s*:\s*([-\d.]+)\]")

# ---------------------------------------------------------------- telemetry

def track_from_srt(path):

    """Return (Nx2 array of local metres, Nx1 array of seconds) or None."""

    text = open(path, "r", errors="ignore").read()

    lats = [float(m) for m in SRT_LAT.findall(text)]

    lons = [float(m) for m in SRT_LON.findall(text)]

    if len(lats) < 4 or len(lats) != len(lons):

        return None

    lat0 = math.radians(np.mean(lats))

    # equirectangular projection, fine over a survey-sized area

    east = (np.array(lons) - np.mean(lons)) * 111320.0 * math.cos(lat0)

    north = (np.array(lats) - np.mean(lats)) * 110540.0

    return np.column_stack([east, north])

# ------------------------------------------------------------------ visual

def track_from_video(cap, sample_fps, width):

    """Accumulate similarity transforms into a rough ground track.

    World frame is aligned to the first frame's heading, so 'bottom-left' is

    relative to the drone's initial orientation, not to true north.

    """

    src_fps = cap.get(cv2.CAP_PROP_FPS) or 30.0

    step = max(1, int(round(src_fps / sample_fps)))

    orb = cv2.ORB_create(1500)

    matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)

    positions, frame_idx = [], []

    pos = np.zeros(2)

    heading = 0.0 # cumulative yaw, radians

    prev_kp = prev_des = None

    scale = None

    i = 0

    while True:

        ok = cap.grab()

        if not ok:

            break

        if i % step:

            i += 1

            continue

        ok, frame = cap.retrieve()

        if not ok:

            break

        if scale is None:

            scale = width / float(frame.shape[1])

        small = cv2.resize(frame, None, fx=scale, fy=scale)

        gray = cv2.cvtColor(small, cv2.COLOR_BGR2GRAY)

        gray = cv2.createCLAHE(2.0, (8, 8)).apply(gray)

        kp, des = orb.detectAndCompute(gray, None)

        if prev_des is not None and des is not None and len(des) > 10:

            matches = matcher.match(prev_des, des)

            if len(matches) >= 12:

                matches = sorted(matches, key=lambda m: m.distance)[:400]

                src = np.float32([prev_kp[m.queryIdx].pt for m in matches])

                dst = np.float32([kp[m.trainIdx].pt for m in matches])

                M, _ = cv2.estimateAffinePartial2D(

                    src, dst, method=cv2.RANSAC, ransacReprojThreshold=3.0

                )

                if M is not None:

                    # scene shift in image space; camera moves the other way

                    dx, dy = -M[0, 2], -M[1, 2]

                    dyaw = math.atan2(M[1, 0], M[0, 0])

                    c, s = math.cos(heading), math.sin(heading)

                    # rotate into world frame, flip y so +y is "up" on the map

                    pos = pos + np.array([c * dx - s * dy, -(s * dx + c * dy)])

                    heading += dyaw

        positions.append(pos.copy())

        frame_idx.append(i)

        prev_kp, prev_des = kp, des

        i += 1

    return np.array(positions), np.array(frame_idx)

# ------------------------------------------------------------------ corners

def order_clockwise_from_bottom_left(box, centre):

    """Order 4 points BL -> TL -> TR -> BR in a y-up coordinate frame."""

    ang = np.array([math.atan2(p[1] - centre[1], p[0] - centre[0]) % (2 * math.pi)

                    for p in box])

    target = 5 * math.pi / 4 # 225 deg = bottom-left

    diff = np.abs((ang - target + math.pi) % (2 * math.pi) - math.pi)

    start = int(np.argmin(diff))

    order = list(np.argsort(-ang)) # clockwise = decreasing angle

    k = order.index(start)

    return [box[j] for j in order[k:] + order[:k]]

def corner_samples(track):

    """Indices into `track` of the four survey-area corners, BL-first clockwise."""

    pts = track.astype(np.float32)

    rect = cv2.minAreaRect(pts)

    box = cv2.boxPoints(rect)

    ordered = order_clockwise_from_bottom_left(box, np.array(rect[0]))

    return [int(np.argmin(np.linalg.norm(pts - c, axis=1))) for c in ordered]

def grab_frame(cap, index):

    cap.set(cv2.CAP_PROP_POS_FRAMES, index)

    ok, frame = cap.read()

    return frame if ok else None

# --------------------------------------------------------------------- main

def main():

    ap = argparse.ArgumentParser()

    ap.add_argument("video")

    ap.add_argument("-o", "--outdir", default="corners")

    ap.add_argument("--srt", help="telemetry sidecar; defaults to VIDEO.srt if present")

    ap.add_argument("--fps", type=float, default=2.0, help="sampling rate")

    ap.add_argument("--width", type=int, default=640, help="analysis width in px")

    args = ap.parse_args()

    cap = cv2.VideoCapture(args.video)

    if not cap.isOpened():

        sys.exit(f"cannot open {args.video}")

    total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))

    srt = args.srt or os.path.splitext(args.video)[0] + ".srt"

    track = track_from_srt(srt) if os.path.exists(srt) else None

    if track is not None:

        mode = "telemetry"

        frame_idx = np.linspace(0, max(total - 1, 0), len(track)).astype(int)

    else:

        mode = "visual"

        track, frame_idx = track_from_video(cap, args.fps, args.width)

        if len(track) < 8:

            sys.exit("not enough usable frames to estimate a track")

    picks = corner_samples(track)

    labels = ["1-bottom-left", "2-top-left", "3-top-right", "4-bottom-right"]

    os.makedirs(args.outdir, exist_ok=True)

    print(f"mode: {mode} samples: {len(track)}")

    for label, s in zip(labels, picks):

        fi = int(frame_idx[s])

        frame = grab_frame(cap, fi)

        if frame is None:

            print(f"{label}: frame {fi} unreadable")

            continue

        out = os.path.join(args.outdir, f"{label}.jpg")

        cv2.imwrite(out, frame)

        t = fi / (cap.get(cv2.CAP_PROP_FPS) or 30.0)

        print(f"{label}: frame {fi} t={t:7.2f}s xy=({track[s][0]:.1f}, {track[s][1]:.1f}) -> {out}")

    cap.release()

if __name__ == "__main__":

    main()

 

# Results:

mode: visual samples: 1100

1-bottom-left: frame 4065 t= 135.50s xy=(-252.0, -1591.2) -> corners\1-bottom-left.jpg

2-top-left: frame 2475 t= 82.50s xy=(-1809.0, 317.5) -> corners\2-top-left.jpg

3-top-right: frame 1275 t= 42.50s xy=(-371.8, 1783.0) -> corners\3-top-right.jpg

4-bottom-right: frame 4560 t= 152.00s xy=(261.0, -695.0) -> corners\4-bottom-right.jpg

   

  

 Pangram Model Architecture and Multi-Objective Training

The underlying software architecture has transitioned across iterations to support increasingly complex text inputs. Its modern core (manifested in Pangram 4) is built upon a large, open-weight Mixture of Experts (MoE) backbone model adapted for sequence classification. The system attaches independent, custom linear classification heads to the final sequence position of the shared backbone, exploiting causal attention mechanisms where the final hidden state vector ($\mathbf{h}_S$) retains a complete contextual representation of the input window.

The system achieves granular, single-pass evaluation by simultaneously optimizing for multiple objectives across distinct classification heads:

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• Segment-Level Edits: Evaluates localized adjustments within the passage.

• Mixed-Authorship Binary Classification: Determines whether a document is fully human-written, fully AI-generated, or hybrid.

• Humanizer Detection: Flags signatures typical of commercial obfuscation or adversarial paraphrasing algorithms.

• Tokenwise Provenance: Projects predictions down to individual token positions using a localized sequence head. To allow every supervised token to utilize context from the complete source sequence under a causal backbone, the framework implements a context replication format known as Repeat2, where each 512-token training window is repeated twice and loss calculations are applied only to the second instance.

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The software stack utilizes standard deep learning frameworks, specifically PyTorch and Hugging Face libraries, optimized via Parameter-Efficient Fine-Tuning (PEFT) methodologies like Low-Rank Adaptation (LoRA). Training is performed across distributed clusters of hardware, such as NVIDIA H100 GPUs, to handle the vast parameter scale required to map modern frontier models.

Prediction Mechanics and Platform Integration

When raw text is passed to the platform via its user interface or REST API, the system does not emit an arbitrary boolean result. Instead, it tokenizes the string, maps the tokens to vector embeddings, and processes them through the neural network to output continuous numerical scores representing spatial coordinates in "Pangram Space".

The system segmentizes documents longer than a specific threshold (e.g., 450 tokens) to assess moving windows individually. The resulting output maps text into calibrated probabilistic thresholds:

Score Range Classification Category

$\le 0.25$ Human-Written

$0.25 < \text{Score} < 0.50$ Lightly AI-Assisted

$0.50 \le \text{Score} < 0.75$ Moderately AI-Assisted

$\ge 0.75$ Fully AI-Generated

The platform's software engineering emphasizes broad downstream availability to achieve its mission of content validation across the broader internet ecosystem. The core model is exposed via high-throughput API endpoints priced dynamically by word count metrics. To operationalize these capabilities directly within existing workflows, the software is deployed via deep software integrations into learning management systems (such as Canvas LMS and Google Classroom), digital publishing platforms (such as Substack), browser extensions for real-time web monitoring, and document verification add-ons like Google Docs.

Through this multi-tiered implementation—spanning structured data engineering, sophisticated multi-head transformer architectures, and extensive platform integrations—Pangram establishes a deterministic legibility framework to handle the challenges of mixed-authorship content at scale.

 Pixel-Level Distribution Analysis in Image Detection

When expanding into visual provenance via Pangram Image, the platform abandons high-level semantic analysis in favor of tracking low-level pixel distribution anomalies. Generative adversarial networks (GANs) and diffusion models (such as Midjourney, Stable Diffusion, or DALL-E) synthesize images through mathematical upscaling or iterative denoising passes. While these techniques generate visually convincing global semantics, they introduce artificial artifacts into the high-frequency spatial frequencies and statistical structures of local pixel values. [6]

1. Statistical Discontinuity Tracking

Human-captured digital imagery is fundamentally shaped by physical sensor physics, including photon shot noise and continuous lens-transfer functions. Conversely, AI-generated images exhibit uniform statistical patterns or unnatural discontinuities due to structural upsampling operators (such as transposed convolutions). Pangram’s image model analyzes these distributions across three vectors:

                 ┌──► Spatial Co-occurrence Matrix (Pixel-to-neighbor joint distribution)

                 │

[Input Image] ───┼──► Discrete Cosine Transform (DCT) (Fourier-domain frequency profiling)

                 │

                 └──► Local Variance Descriptors (Chrominance-to-luminance edge behavior)

● Spatial Co-occurrence Matrices: The software computes joint probability distributions between neighboring pixel intensities across localized windows. Synthetic images frequently display overly uniform pixel transitions or grid-like periodicities that do not occur in natural sensor noise.

● Fourier and Frequency Domain Profiling: Images undergo automated Discrete Cosine Transforms (DCT) to isolate high-frequency bands. Diffusion models leave structural footprints—often referred to as "spectral scars"—visible as anomalous energy spikes at specific coordinates within the high-frequency spectrum.

● Chrominance-Luminance Cross-Correlation: Natural camera lenses disperse light across the RGB channels according to predictable optical behaviors (chromatic aberration). Generative software pipelines calculate these channels independently or via mathematical abstractions, creating distinct, unaligned edge-variance profiles between luminance and chrominance boundaries.

2. Robustness to Downstream Processing

To prevent these pixel-level signatures from being erased by basic compression or resizing algorithms, the classifier head is co-trained using differentiable data augmentation layers. The network learns to map local spatial variances to a normalized invariant space, ensuring the detection architecture maintains a high accuracy ceiling even when images are compressed for digital distribution or social platform publishing.