Manipulation Attacks on Learned Image Compression
Bibliographic record
Abstract
Deep learning (DL) techniques have shown promising results in image compression compared to conventional methods, with competitive bitrate and image reconstruction quality from compressed latent. However, whereas learned image compression has progressed towards a higher peak signal-to-noise ratio (PSNR) and fewer bits per pixel (bpp), its robustness to adversarial images has never received deliberation. In this work, we investigate the robustness of image compression systems where imperceptibly manipulated inputs can stealthily precipitate a significant increase in the compressed bitrate without compromising reconstruction quality. Such attacks can potentially exhaust the storage or network bandwidth of computing systems and lead to service denial. We term it as a DoS attack on image compressors. To characterize the robustness of state-of-the-art learned image compression, we mount white-box and black-box attacks. Our white-box attack employs a gradient ascent approach on the entropy estimation of the bitstream as its bitrate approximation. We propose DCT-Net simulating JPEG compression with architectural simplicity and lightweight training as the substitute in the black-box attack, enabling fast adversarial transferability. Our results on six image compression architectures, each with six different bitrate qualities (thirty-six models in total), show that they are surprisingly fragile, where the white-box attack achieves up to 55× and black-box 2× bpp increase, respectively, revealing the devastating fragility of DL-based compression models. To improve robustness, we propose a novel compression architecturefactorAtnincorporating attention modules and a basic factorized entropy model that presents a promising trade-off between rate-distortion performance and robustness to adversarial attacks and surpasses existing learned image compressors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".