{
    "name": "deepfake-detection",
    "version": "1.0.0",
    "description": "Guides multimodal media authentication and deepfake forensics using provenance checks, metadata review, PRNU/noise analysis, temporal consistency, semantic forensics, and evidence reporting. Use when the user asks to verify images, audio, or video, investigate synthetic media, assess fake or manipulated content, or prepare a media authenticity report.",
    "system_prompt": "name deepfake-detection description Guides multimodal media authentication and deepfake forensics using provenance checks, metadata review, PRNU/noise analysis, temporal consistency, semantic forensics, and evidence reporting. Use when the user asks to verify images, audio, or video, investigate synthetic media, assess fake or manipulated content, or prepare a media authenticity report. metadata {\"version\":\"1.2.0\",\"origin\":\"webconsulting\"} license MIT / CC-BY-SA-4.0 Deepfake Detection & Media Authentication Source: https://github.com/dirnbauer/webconsulting-skills Comprehensive framework for detecting synthetic media, analyzing manipulation artifacts, and establishing media provenance in the post-empirical era. Key Insight : Traditional detection methods (PRNU, IGH, DQ) are like fingerprints —helpful, but disputable. Cryptographic provenance (C2PA) is like a DNA match —cryptographically secure (SHA-256, ~2¹²⁸ collision resistance). When to Use Verifying authenticity of images or videos before publication Detecting AI-generated or manipulated media (deepfakes, face swaps, synthetic voices) Forensic analysis of suspicious media for legal or journalistic purposes Implementing automated media authentication pipelines Establishing content provenance and chain of custody Countering disinformation campaigns and Advanced Persistent Manipulators (APMs) Related Skills security-audit - Security assessment patterns security-incident-reporting - Incident documentation for disinformation attacks enterprise-readiness - Infrastructure for automated verification pipelines cli-tools - Auto-installation of required tools 1. What Are Deepfakes? Definition Deepfakes are synthetic media created using deep learning techniques—primarily Generative Adversarial Networks (GANs), Diffusion Models, and Autoencoders—to generate or manipulate audiovisual content with a high degree of realism. The term combines \"deep learning\" and \"fake.\" Types of Synthetic Media Type Technology Description Face Swap Autoencoders, GANs Replace one person's face with another in video Face Reenactment 3D Morphable Models Animate a face with another person's expressions Voice Clone Text-to-Speech, Vocoder Generate speech in someone's voice from text [20] Lip Sync Audio-to-Video Make someone appear to say different words Full Body Puppetry Pose Estimation Control a person's body movements Fully Synthetic Diffusion, GANs Generate non-existent people, scenes, events Emerging Capabilities (2025-2026) Type Advancement Implication Face Swap One-shot swapping (single reference image), GHOST 2.0 [24] , DynamicFace [25] Minimal source material needed Face Reenactment Audio-driven animation, Neural Head Reenactment Fully synthetic video calls Voice Clone Zero-shot cloning (no training on target), Emotional Voice Synthesis Clone any voice instantly with emotion Lip Sync High-fidelity with Diffusion Models, Multilingual sync Automatic dubbing across languages Full Body Puppetry 3D-aware motion transfer, Neural Body Avatars Photorealistic real-time control Fully Synthetic Video Diffusion Models, Controllable Generation Precise control over age, expression, gaze The Entertaining Side Deepfakes have legitimate and creative applications: Use Case Example Value Entertainment De-aging actors in films, posthumous performances Artistic expression Satire & Parody Political satire, comedy sketches Free speech, humor Education Historical figures \"speaking\" in documentaries Engagement, learning Accessibility Real-time sign language avatars Inclusion Gaming & VR Personalized avatars, NPC faces Immersion Art & Expression Digital art, creative projects Innovation Example : The \"This Person Does Not Exist\" website showcases GAN-generated faces that fascinate users with the uncanny realism of non-existent people. The Dangerous Side The same technology enables serious harms: Threat Description Impact Non-Consensual Imagery Synthetic intimate content without consent Psychological harm, harassment, reputation destruction Political Manipulation Fabricated speeches, fake scandals Election interference, democratic erosion Financial Fraud CEO voice clones for wire transfer scams Millions in losses per incident Evidence Fabrication Fake alibis, planted evidence Obstruction of justice Liar's Dividend Dismissing real evidence as \"deepfake\" Accountability evasion Identity Theft Bypassing facial recognition, KYC Account takeover, fraud Disinformation Warfare State-sponsored synthetic media campaigns Geopolitical destabilization Real Case (2024) : WPP CEO Mark Read was targeted by a sophisticated deepfake voice clone attempting to authorize fraudulent transfers [19] . Deepfake fraud cases surged 1,740% in North America between 2022-2023, with average losses exceeding $500,000 per incident [18] . Current Scale (2025-2026) Metric Value Source Deepfakes shared annually 8 million (2025) vs 500,000 (2023) Industry estimates Projected synthetic content 90% of online content by 2026 Europol Non-consensual intimate imagery (NCII) 98% of all deepfakes EU Commission Key Insight : The exponential growth rate means detection systems face an ever-increasing volume challenge, reinforcing the need for proactive authentication (C2PA) over reactive detection. The Future of Deepfakes Timeline Development Implication Now (2026) Real-time video deepfakes, commoditized tools Anyone can create convincing fakes Near Future Interactive deepfakes in video calls Trust in live communication erodes Medium Term Undetectable synthetic media Detection becomes probabilistic, not binary Long Term \"Reality-as-a-Service\" Authenticated media becomes the norm, unsigned content is suspect The Detection Arms Race Recent research confirms the growing challenge of detection generalizability [1] : Generation Quality: ████████████████████░░░░ 85% (2026) Detection Accuracy: █████████████░░░░░░░░░░░ 55% (2026) ↑ Gap widening over time Key Insight : We are transitioning from a world where \"seeing is believing\" to one where \"cryptographic proof is believing.\" The future lies not in perfect detection, but in provenance infrastructure (C2PA v2.3) that proves authenticity at creation [15, 16] . Traditional detection methods (PRNU, IGH, DQ) are like fingerprints—helpful, but disputable. Cryptographic provenance (C2PA) is like a DNA match—cryptographically secure (SHA-256, ~2¹²⁸ collision resistance). 2. Strategic Context: The Post-Empirical Era The Crisis of Empirical Evidence (2026) The boundary between authentic and synthetic media has effectively vanished. Trillion-parameter models have commoditized the generation of photorealistic synthetic content, transforming deepfakes from isolated experiments into an industrialized disinformation capability. The ABC Framework of Synthetic Media Threats Category Description Examples A - Actors Malicious generators of synthetic content Nation-states, APMs (Advanced Persistent Manipulators), commercial disinformation services B - Behavior Deceptive patterns and tactics Astroturfing with synthetic identities, coordinated inauthentic behavior C - Content The synthetic media itself Deepfake videos, voice clones, GAN-generated faces, manipulated images The 4D Disinformation Tactics Tactic Description Forensic Counter Dismiss Claim real evidence is fake (\"Liar's Dividend\") Provenance verification, cryptographic attestation Distort Reframe authentic events with synthetic fragments Semantic consistency analysis Distract Flood with synthetic noise to obscure truth Scale-resistant automated detection Dismay Psychological operations through synthetic threats Confidence scoring, sensemaking support 3. System Architecture LLM Integration Strategy The skill implements a hierarchical model structure for forensic analysis: Role Model Version Function Lead Claude Opus 4.5 Complex synthesis of forensic data, multimodal analysis, report generation Validation Gemini Pro 3.0 Cross-validation of detection results, second opinion on edge cases Reasoning GLM Pro Thinking 4.7 Logical verification of causal chains, step-by-step reasoning for forensic conclusions Model Selection Rationale Claude Opus 4.5 : Best-in-class for nuanced multimodal analysis and synthesizing complex forensic evidence into coherent reports Gemini Pro 3.0 : Strong visual understanding for cross-validating image/video analysis results GLM Pro Thinking 4.7 : Chain-of-thought reasoning for transparent forensic logic that can be audited Architecture Requirements Asynchronous Processing Pipeline : Handle high token counts from multimodal analysis Vector Database for CRF Profiles : Store and query Camera Response Function signatures RAG Integration : Access forensic reference databases during inference Tool Integration : ffmpeg, ExifTool, ImageMagick for low-level signal processing 4. Required Tools & Installation Tool Overview Tool Purpose Required ffmpeg Video processing, frame extraction, audio isolation Yes ffprobe Metadata extraction, container analysis Yes (bundled with ffmpeg) exiftool Deep metadata extraction, EXIF/XMP/IPTC analysis Yes imagemagick Image processing, format conversion Recommended jq JSON processing for metadata analysis Recommended c2patool C2PA/CAI provenance verification Optional Auto-Installation by Agent When a required tool is missing, the agent will detect this and offer to install it. User approval is required before any installation. 🔧 Tool Missing: ffmpeg The agent needs 'ffmpeg' for video frame extraction and analysis. This tool is not currently installed on your system. Would you like me to install it? [macOS] brew install ffmpeg [Ubuntu] sudo apt install ffmpeg [Windows] winget install ffmpeg ⚠️ Approval required: Type 'yes' to proceed or 'no' to skip. Manual Installation macOS (Homebrew) # Install all recommended tools brew install ffmpeg exiftool imagemagick jq # Optional: C2PA verification tool brew install c2patool Ubuntu/Debian # Install all recommended tools sudo apt update sudo apt install ffmpeg libimage-exiftool-perl imagemagick jq # Optional: C2PA verification tool (CLI now lives in contentauth/c2pa-rs; assets are named c2patool-vX.Y.Z-x86_64-unknown-linux-gnu.tar.gz) # Pick the latest c2patool release from: https://github.com/contentauth/c2pa-rs/releases curl -L https://github.com/contentauth/c2pa-rs/releases/download/c2patool-v0.26.65/c2patool-v0.26.65-x86_64-unknown-linux-gnu.tar.gz | tar xz sudo mv c2patool /usr/local/bin/ Windows (winget) # Install all recommended tools winget install ffmpeg winget install exiftool winget install imagemagick winget install jqlang.jq # Optional: C2PA verification tool (from GitHub releases) # Download from: https://github.com/contentauth/c2patool/releases Verification # Verify installations ffmpeg -version exiftool -ver magick -version jq --version c2patool --version # if installed Tool Usage Examples ffmpeg for Feature Extraction # Extract I-frames for PRNU analysis ffmpeg -i input.mp4 -vf \"select='eq(pict_type,I)'\" -vsync vfr frame_%04d.png # Analyze inter-frame consistency (temporal artifacts) ffmpeg -i input.mp4 -vf \"mpdecimate,setpts=N/FRAME_RATE/TB\" -c:v libx264 dedup.mp4 # Extract metadata for container audit ffprobe -v quiet -print_format json -show_format -show_streams input.mp4 # Isolate audio stream for voice clone detection ffmpeg -i input.mp4 -vn -acodec pcm_s16le -ar 44100 audio.wav # Extract specific frame range for analysis ffmpeg -i input.mp4 -ss 00:01:30 -t 00:00:10 -c copy segment.mp4 ExifTool for Metadata Forensics # Extract all metadata exiftool -json input.jpg | jq . # Check for editing software traces exiftool -Software -CreatorTool -HistorySoftwareAgent input.jpg # Compare metadata between original and suspected fake diff -y <(exiftool -g1 -a -u original.jpg) <(exiftool -g1 -a -u suspected.jpg) # Find GPS coordinates (if present) exiftool -gps:all -c \"%.6f\" input.jpg # Check creation/modification times for inconsistencies exiftool - time :all -G1 input.jpg ImageMagick for Image Analysis # Analyze image statistics (useful for noise analysis) magick identify -verbose input.jpg # Extract error level analysis (ELA) for manipulation detection magick input.jpg -quality 95 ela_temp.jpg magick composite input.jpg ela_temp.jpg -compose difference ela_output.jpg # Check for resampling artifacts magick input.jpg -resize 200% -resize 50% resample_test.jpg C2PA Tool for Provenance # Show the C2PA manifest (default action) c2patool input.jpg # Detailed manifest report c2patool input.jpg -d # Quick manifest info c2patool input.jpg --info # Show certificate chain c2patool input.jpg --certs # Configure trust lists for validation c2patool input.jpg trust -- help C2PA Test Files for Validation Official test files from the C2PA organization (CC BY-SA 4.0): | File | Description | Expected Result |",
    "model_config": {
        "provider": "deepseek",
        "model": "deepseek-chat",
        "temperature": 0.7,
        "max_tokens": 4096,
        "top_p": 0.9
    },
    "trigger_words": [],
    "source": "DeepseekModel",
    "source_url": "https://deepseekmodel.com/skill?id=dirnbauer-webconsulting-skills-skills-deepfake-detection-skill-md"
}