Decrypting the Chao-based Image Encryption via A Deep Autoencoding Approach
Bibliographic record
Abstract
In this work, as a proof of concept, we develop a simple yet effective deep autoencoding approach to attack the chaos-based image encryption algorithm. Specifically, we first project chaos-based encrypted images into the low-dimensional feature space. In the manifold, essential information of plain images can be largely preserved. A deconvolutional generator is then utilized to regenerate decrypted images perceptually similar to plain images in the high-dimensional image space. For the first time, we show the feasibility of attacking chaos-based encryption methods in a key-independent manner, which is fundamentally different from traditional image encryption attacks. Given chaos-based encrypted images, a well-trained decryption model can automatically reconstruct plain images with high visual fidelity. In both static-key and dynamic-key experiments, we successfully attack several chaos-based algorithms on the MNIST dataset and show that decrypted images are perceptually recognizable to both human eyes and intelligent models. Importantly, this study demonstrates the potential of employing deep learning approaches to crack image encryption algorithms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".