VERIDEX: Analysis of a Browser-Based Real-Time AI Deepfake Detection System

VERIDEX is a real-time AI deepfake detection engine that runs entirely in the browser. Its core features include: completing image authenticity verification in 3 seconds, no need for servers/data transmission/registration, using 10 forensic signal algorithms and ensemble learning technology to achieve a privacy-first detection experience. Its open-source architecture supports local computing, providing an efficient and secure solution for combating deepfakes.

The development of generative AI technology has led to the rapid spread of deepfake content (e.g., AI faces, synthetic videos), bringing social impacts. Traditional cloud-based detection solutions have issues such as privacy leakage risks, data transmission delays, and server dependency, which cannot meet the needs of immediacy and privacy protection.

VERIDEX is built with a pure front-end technology stack and zero-dependency design:

• Core modules: Landing page, detection engine, analysis dashboard
• Technology selection: Native HTML5/CSS3/JS (ES6+), Chart.js (CDN imported), Canvas 2D API (image processing), localStorage (data storage), Netlify (static deployment) This architecture ensures high accessibility; users only need a modern browser to use it.

VERIDEX uses the idea of ensemble learning and combines 10 complementary forensic signal algorithms:

1. Pixel and sensor noise detection
2. FFT spectrum pattern analysis (captures GAN artifacts)
3. ELA error level analysis (reveals features of no editing history)
4. Skin and facial texture detection (identifies "plastic-like" skin)
5. Color histogram analysis (abnormal color distribution)
6. Hand anatomical structure detection (finger/joint errors)
7. Physical consistency of lighting (reasonableness of shadows/highlights)
8. Background coherence analysis (repeated textures/depth issues)
9. Text and geometry detection (curved straight lines/incorrect vanishing points)
10. Weighted aggregation (extra weighting on hand/lighting signals, 34% threshold decision)

VERIDEX has continuous evolution capabilities:

• User feedback loop: After users confirm results, the system adjusts signal weights with a 1.8% learning rate.
• Claude AI analysis: After accumulating 3 feedbacks, it calls the Claude API to analyze scan history, identify generator types (Midjourney/DALL-E, etc.), recommend weight configurations, and explain adjustment reasons.

Performance Optimization:

• Image downsampling to a maximum of 512px to balance speed and accuracy;
• ELA analysis limited to 256px, single JPEG compression (quality 0.75), 3x speed improvement;
• Weights stored in localStorage and automatically normalized (sum to 1.0).

Privacy Protection: All computations are done locally with no data upload, achieving zero leakage risk, real-time response, offline availability (except Claude functions), and no need for an account.

Application Scenarios:

• News media: Verify image authenticity;
• Social platforms: User self-check tool;
• Educational institutions: AI literacy demonstration;
• Enterprise compliance: Sensitive document screening.

Future Plans:

• Real-time video stream detection;
• Specialized identification of more generators;
• Enhanced robustness against adversarial samples;
• Integration with privacy computing technology.

VERIDEX demonstrates the potential of browser-based AI applications. It achieves professional-level detection capabilities through front-end technology, and its privacy-first concept provides a reference for AI ethical design. The project is open-source under the MIT license, hosted on Netlify, and welcome to experience it.