atlas

Research map / Adversarial robustness and security / Attacks on text and graphs

Image Shortcut Squeezing: Countering Perturbative Availability Poisons with Compression

ICML 2023 · Adversarial robustness and security › Attacks on text and graphs · approach: Membership inference and inversion

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Grayscale compression of training data (ISS-Gray)EM, DC, REM poisons (low-frequency/color-varying perturbations) · CIFAR-10 L_inf=8clean test accuracy: EM 21.05 -> 93.01Helped
JPEG (quality 10) compression of training data (ISS-JPEG)High-frequency surrogate-trained poisons (TAP, SEP, HYPO) · CIFAR-10 L_inf=8clean test accuracy: TAP 8.17 -> 83.87; SEP 3.85 -> 84.37Helped
Gray+JPEG combined compression without knowing the poison typeAll 12 PAP methods · CIFAR-10 L_inf=8clean test accuracy: globally effective, with a small decrease in clean accuracyHelped
Data augmentations (Cutout, CutMix, Mixup, Gaussian) applied to training dataPAP poisons generally · CIFAR-10 L_inf=8clean test accuracyNo clear effect
Adversarial training as countermeasure vs ISSL_inf and L_2 PAPs · CIFAR-10clean test accuracyNo clear effect
Adversarial training replaced by ISS (lower cost)CIFAR-10 countermeasure training cost · CIFAR-10training time: ISS training time is only 1/7 of AT training timeHelped
Median filtering applied to training dataOPS (one-pixel) poisons · CIFAR-10 L_0=1clean test accuracy: OPS 36.55 -> 85.16Helped
Gray compression applied to both training and test data (Gray-TT) vs training only (ablation)EM and other poisons · CIFAR-10 L_inf=8clean test accuracy: Gray-TT 90.75 vs Gray 93.01 on EMNo clear effect
Adding partial/fraction poisoning (10-90%) with ISS Gray/JPEGPoisons at 80% poisoning proportion · CIFAR-10 partial poisoningclean test accuracyNo clear effect
Adaptive grayscale-aware EM poisons (EM-Gray) vs ISS Gray (ablation)EM poisons · CIFAR-10 L_inf=8 adaptiveclean test accuracy: EM-Gray 16.60 under Gray vs EM 93.01 non-adaptiveHurt
Adaptive JPEG-aware EM poisons (EM-JPEG) vs ISS JPEG (ablation)EM poisons · CIFAR-10 L_inf=8 adaptiveclean test accuracy: EM-JPEG 83.11 vs EM 81.50 under JPEGNo clear effect
Equal-weight mix of EM and TAP perturbations defended by ISS (ablation)EM+TAP combined poisons · CIFAR-10 L_inf=8clean test accuracy: EM+TAP w/o 36.07 -> JPEG 84.62; Gray 18.93Helped

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy