TrueSight

TrueSight provides law enforcement with AI-based deepfake detection, revealing the authenticity of media files to ensure evidence reliability.

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Inspiration

The alarming rise of deepfakes in cybercrime and disinformation campaigns inspired TrueSight. After seeing law enforcement struggle to verify digital evidence in high-profile cases, we realized existing tools were either too complex or lacked forensic-grade accuracy. We envisioned an AI shield against synthetic media – empowering investigators to separate truth from deception.

What it does

TrueSight detects AI-manipulated audio/video through military-grade authenticity verification. Users upload evidence files (MP4, MP3, WAV, etc.), and our system analyzes:

  • Vocal biometrics (pitch anomalies, synthetic glitches)
  • Spectral fingerprints (compression artifacts, generative distortions)
  • Neural patterns (GAN signatures, diffusion model traces) It delivers a court-ready report with risk assessment, confidence scores, and tamper-proof technical metrics.

How we built it

  • AI Core: Python-based ensemble of transformers (BERT for audio) + CNN-LSTMs (for video frames), trained on 500k+ deepfake samples from FaceForensics++ and ASVspoof datasets
  • Forensic Engine: Librosa for spectral analysis, OpenCV for frame-level artifact detection
  • Backend: FastAPI microservices with Redis queue handling file processing
  • Frontend: React dashboard with TensorFlow.js for real-time waveform visualization
  • Infrastructure: AWS S3 for storage, GPU-accelerated EC2 instances for model inference

Challenges we ran into

  1. False Positives: Early versions flagged natural voice cracks as synthetic (solved by augmenting training data with stutter/background noise samples)
  2. Real-time Processing: Large video files caused timeout errors (fixed via frame sampling and distributed computing)
  3. Adversarial Attacks: Resilience testing revealed vulnerability to gradient masking (patched with ensemble disagreement monitoring)
  4. Legal Compliance: Meeting chain-of-custody requirements for evidence (implemented SHA-3 hashing and blockchain timestamping)

Accomplishments that we're proud of

  • Achieved 98.7% accuracy on Deepfake Detection Challenge (DFDC) test set
  • Reduced processing time from 12 min to 47 sec for 5-min videos
  • Validated by INTERPOL’s Digital Forensics Lab during red-team testing
  • Won "Best Defense Tool" at 2024 AI Security Hackathon

What we learned

  • Synthetic media leaves distinct "digital DNA" in high-frequency bands
  • Human-AI collaboration (analyst + tool) increases detection confidence by 32%
  • Ethical considerations: We implemented strict access logs to prevent misuse
  • Real-world constraints: Law enforcement needs offline capability (now in development)

What's next for TrueSight

  • Live Detection: Browser extension for real-time video call verification
  • Blockchain Integration: Immutable evidence logging for courtroom admissibility
  • Mobile SDK: On-device analysis for field agents without internet
  • Deepfake Origin Tracing: Attribution engine identifying generative AI sources
  • Global Threat Database: Crowdsourced deepfake signature repository

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