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

Learning Transferable Adversarial Perturbations

NeurIPS 2021 · Adversarial robustness and security › Attacks on text and graphs · approach: Defenses against universal perturbations · 27 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
train generator with mid-level feature separation loss instead of cross-entropy-based CDA lossperturbation generator, ImageNet-trained · ImageNet val black-box averagefooling rate (%): 76.5 -> 86.8Helped
train generator with feature separation loss instead of CDA loss on Comics data (no target data)perturbation generator · ImageNet val strict black-box averagefooling rate (%): 80.5 -> 90.8Helped
use ChestX (large domain gap) as generator training data with feature separation loss instead of CDAperturbation generator · ImageNet val strict black-box averagefooling rate (%): 40.6 -> 62.0Helped
train generator on ChestX or Comics data instead of ImageNet-like data, same loss (ablation)Ours · ImageNet val strict black-box averagefooling rate (%): 90.8 (Comics) -> 62.0 (ChestX)Hurt
extreme black-box: classifier trained on ChestX instead of ImageNet (ablation)Ours · ImageNet val averagefooling rate (%): 87.0 -> 68.5Hurt
use feature separation loss instead of CDA against adversarially trained defencesSqueezeNet generator · EnsembleAdv, HGD, R&Ptop-1 error increase (%): HGD 31.4 -> 43.9Helped
use feature separation loss instead of CDA against PGD and FD defencesSqueezeNet generator · FD and PGDtop-1 error increase (%): FD 4.06 -> 7.06; PGD 5.42 -> 3.30Mixed
use feature separation loss instead of CDA to attack object detectors (cross-task)perturbation generator, ImageNet-trained · PASCAL VOC SSD averagemAP: 14.4 -> 9.31Helped
attack the initial layers instead of mid-level layers (ablation)generator · transfer attacksrequired perturbation strengthHurt
attack top-level features instead of mid-level features (ablation)generator · transfer to different data or architecturesfooling rateHurt

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