Synthetic Media Is Everywhere
The term 'deepfake' undersells the scope of the problem. Synthetic media now includes AI-generated images (Midjourney, DALL-E, Stable Diffusion), AI-generated video (Sora, Runway, Pika), voice clones (ElevenLabs, Resemble AI), synthetic text (GPT-5, Claude, Gemini), and hybrid media combining real and synthetic elements. Detection can no longer focus on face swaps alone. A comprehensive detection system must identify synthetic elements across all media types, including content that blends authentic and generated components.
Detection Method: Spectral Analysis
GAN-generated images leave distinctive artifacts in the frequency domain. When you apply a Fourier transform to a GAN-generated image, periodic patterns emerge that are absent in photographs. These patterns result from the upsampling operations in generator architectures. DRD's spectral analysis pipeline converts images to the frequency domain using 2D FFT, applies bandpass filters to isolate GAN-characteristic frequencies, computes power spectral density features, and classifies using a lightweight CNN trained on 2 million labeled samples. This method achieves 92% accuracy on current-generation GANs and is particularly effective at detecting StyleGAN and its derivatives.
Detection Method: GAN Fingerprinting
Each GAN architecture leaves a unique fingerprint — subtle statistical patterns that act like a digital signature for the model that generated the content. DRD maintains a fingerprint library for over 150 known generative models, updated monthly as new architectures emerge. When content is submitted for analysis, DRD extracts statistical features from pixel-level noise patterns, compares against the fingerprint library to identify the likely source model, and assigns a confidence score based on fingerprint match strength. GAN fingerprinting is complementary to spectral analysis — it identifies not just that content is synthetic, but which specific model created it.
Detection Method: Temporal Consistency
For video, temporal consistency analysis is the most reliable detection signal. Current video generation models struggle with maintaining physical plausibility across frames. DRD's temporal analysis checks for object permanence violations (objects appearing or disappearing without cause), physics inconsistencies (impossible shadows, gravity violations, fluid behavior), identity drift (subtle changes in facial features frame to frame), and background stability (warping or shifting in areas that should be static). These artifacts are often invisible to casual viewers but are reliably detectable through automated analysis. The temporal consistency detector achieves 96% accuracy on AI-generated video, making it DRD's most effective single detection method.
The Multi-Layered Pipeline
No single detection method is sufficient. DRD combines all methods in a multi-layered pipeline that processes content through spectral analysis, GAN fingerprinting, temporal consistency (for video), metadata analysis (EXIF data, compression artifacts, creation tool signatures), and provenance verification (C2PA Content Credentials check). Each layer produces an independent confidence score. These scores are combined using a calibrated ensemble model that accounts for correlations between methods and adjusts for content type. The final output is a synthetic probability score from 0-100, a list of detected synthetic indicators with per-method confidence, and an identified source model (when GAN fingerprinting matches).
Provenance Over Detection
DRD's long-term strategy prioritizes provenance over detection. Detection is inherently reactive — it tries to catch synthetic content after creation. Provenance is proactive — it ensures authentic content can always be verified. As generation models improve, detection accuracy will fluctuate. But provenance guarantees are cryptographic and permanent. DRD recommends a defense strategy that uses detection for triage (flagging suspicious content for review), provenance for verification (confirming authentic content is authentic), and policy for enforcement (defining organizational rules for handling detected and unverified content). This layered approach ensures robust protection even as synthetic media technology advances.
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