Securing Digital Images with AI-Augmented NonNegative Matrix Factorization for Tamper Detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A secure hashing method and AI-enhanced Non-Negative Matrix Factorization (NMF) form the foundation of this study’s strong image authentication and tampering detection framework. Hamming distance comparisons between generated image hashes are used by the suggested system to reliably verify and effectively detect image manipulation. The framework is tested on the GenImage and CASIA datasets, focusing on challenging tasks like cross-generator image identification and degraded image classification. It undergoes testing in difficult circumstances, such as image compression, blurring, and low resolution. Using the GenImage dataset, experimental results show that the suggested method achieves exceptional classification accuracy $97.21 \%$, confirming its effectiveness in practical settings.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it