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VIDRAFT_LAB

SeaWolf-AI
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reacted to their post with 👍 about 1 hour ago
AI Is Training on Your Content Without Permission — Fight Back with Invisible Watermarks https://huggingface.co/spaces/FINAL-Bench/security-scan Most generative AI training data is crawled without consent. Your text gets summarized, images reprocessed, videos clipped — with no way to prove you're the original creator. Existing watermarks are either visible or wiped out by a single AI preprocessing pass. Detect Before, Track After Pre-embed — Detect theft without any watermark. Text plagiarism check, image similarity analysis (perceptual hash, SSIM, color histogram, feature matching), and video temporal matching catch copies, edits, and excerpts. Post-embed — Embed invisible multi-layer watermarks. If one layer is destroyed, others survive independently. Even full removal leaves forensic traces as evidence. Text: 4 Independent Layers Four mechanisms work simultaneously: zero-width Unicode characters at morpheme/word boundaries (Korean Kiwi + English NLP), style fingerprinting via synonym-ending-connective substitution, SHA-256 timestamped evidence packages, and punctuation-anchored micro-marks. Each layer uses a different Unicode category, so attacks on one cannot eliminate the others. Full bilingual support, zero readability impact. 34-Attack Defense 7 categories, 34 attacks simulated: Unicode normalization, invisible character removal, homoglyph substitution (9,619 confusables), and AI rewriting. Each scored on Signal (watermark survival) + Trace (forensic evidence of attack) — proving deliberate removal even when watermarks are destroyed. Image & Video Images: DCT frequency-domain watermarks surviving JPEG compression and resize. Videos: keyframe watermarking with temporal propagation and majority-vote extraction. Both support pre-embed similarity detection. Who Is This For Creators, rights holders needing legal evidence, media companies, and organizations tracking document leaks. Korean/English bilingual, open source, Gradio-based.
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FINAL-Bench/security-scan
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AI Is Training on Your Content Without Permission — Fight Back with Invisible Watermarks https://huggingface.co/spaces/FINAL-Bench/security-scan Most generative AI training data is crawled without consent. Your text gets summarized, images reprocessed, videos clipped — with no way to prove you're the original creator. Existing watermarks are either visible or wiped out by a single AI preprocessing pass. Detect Before, Track After Pre-embed — Detect theft without any watermark. Text plagiarism check, image similarity analysis (perceptual hash, SSIM, color histogram, feature matching), and video temporal matching catch copies, edits, and excerpts. Post-embed — Embed invisible multi-layer watermarks. If one layer is destroyed, others survive independently. Even full removal leaves forensic traces as evidence. Text: 4 Independent Layers Four mechanisms work simultaneously: zero-width Unicode characters at morpheme/word boundaries (Korean Kiwi + English NLP), style fingerprinting via synonym-ending-connective substitution, SHA-256 timestamped evidence packages, and punctuation-anchored micro-marks. Each layer uses a different Unicode category, so attacks on one cannot eliminate the others. Full bilingual support, zero readability impact. 34-Attack Defense 7 categories, 34 attacks simulated: Unicode normalization, invisible character removal, homoglyph substitution (9,619 confusables), and AI rewriting. Each scored on Signal (watermark survival) + Trace (forensic evidence of attack) — proving deliberate removal even when watermarks are destroyed. Image & Video Images: DCT frequency-domain watermarks surviving JPEG compression and resize. Videos: keyframe watermarking with temporal propagation and majority-vote extraction. Both support pre-embed similarity detection. Who Is This For Creators, rights holders needing legal evidence, media companies, and organizations tracking document leaks. Korean/English bilingual, open source, Gradio-based.
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