AI Is Training on Your Content Without Permission — Fight Back with Invisible Watermarks
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AI Is Training on Your Content Without Permission — Fight Back with Invisible Watermarks
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https://huggingface.co/spaces/FINAL-Bench/security-scan
FINAL-Bench/security-scan
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https://huggingface.co/spaces/FINAL-Bench/security-scan
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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.
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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.
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Detect Before, Track After
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Detect Before, Track After
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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.
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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.
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Post-embed — Embed invisible multi-layer watermarks. If one layer is destroyed, others survive independently. Even full removal leaves forensic traces as evidence.
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Post-embed — Embed invisible multi-layer watermarks. If one layer is destroyed, others survive independently. Even full removal leaves forensic traces as evidence.
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 Text: 4 Independent Layers
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 Text: 4 Independent Layers
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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.
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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.
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34-Attack Defense
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34-Attack Defense
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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.
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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.
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Image & Video
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Image & Video
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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.
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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.
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Who Is This For
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Who Is This For
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Creators, rights holders needing legal evidence, media companies, and organizations tracking document leaks. Korean/English bilingual, open source, Gradio-based.
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Creators, rights holders needing legal evidence, media companies, and organizations tracking document leaks. Korean/English bilingual, open source, Gradio-based.
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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