RDIAS: Robust and Decentralized Image Authentication System
Bibliographic record
Abstract
Recent AI tools can subtly manipulate images, eroding users’ trust in the authenticity of images they see on their displays. Current image authentication methods either detect artifacts that may result from manipulations or attach hashes of images as metadata for users to verify. The efficacy of the first approach is rapidly deteriorating with the continuous improvements in AI tools, leading to missing many serious manipulations. Hashes become invalid once images are subjected to any processing, such as re-sizing and transcoding. This makes the second approach impractical as most platforms, e.g., Facebook and X, perform several legitimate operations on images. Further, most platforms remove the metadata attached to images. We propose RDIAS, a robust and practical image authentication system. RDIAS securely embeds representative fingerprints into images without damaging their visual quality. We design these fingerprints to robustly detect malicious manipulations, e.g., adding/removing objects, while tolerating legitimate operations, e.g., image resizing and transcoding. Rigorous evaluation of RDIAS with diverse image datasets and realistic manipulations conducted by human subjects utilizing AI tools shows its high accuracy and efficiency. For example, RDIAS detects DeepFake manipulations that change facial features/expressions with an accuracy of 99%. The results also show that RDIAS preserves image quality and verifies authenticity in real time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".