Papers › An Orthogonal Classifier for Improving the Adversarial Robustness of Neural Networks

An Orthogonal Classifier for Improving the Adversarial Robustness of Neural Networks

19 May 2021arXiv:2105.09109archive 2025-07-28

Cong Xu, Xiang Li, Min Yang

Neural networks are susceptible to artificially designed adversarial perturbations. Recent efforts have shown that imposing certain modifications on classification layer can improve the robustness of the neural networks. In this paper, we explicitly construct a dense orthogonal weight matrix whose entries have the same magnitude, thereby leading to a novel robust classifier. The proposed classifier avoids the undesired structural redundancy issue in previous work. Applying this classifier in standard training on clean data is sufficient to ensure the high accuracy and good robustness of the model. Moreover, when extra adversarial samples are used, better robustness can be further obtained with the help of a special worst-case loss. Experimental results show that our method is efficient and competitive to many state-of-the-art defensive approaches. Our code is available at \url{https://github.com/MTandHJ/roboc}.

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conv1x1 MTandHJ/roboc/models/resnet.py official repository unverified MIT (permissive) · 94eee0c533880ae7 · report
conv3x3 MTandHJ/roboc/models/resnet.py official repository unverified MIT (permissive) · 3600f032ac79fc6b · report
enter_attack_exit MTandHJ/roboc/src/base.py official repository unverified MIT (permissive) · d44273824b416e40 · report
generate_weights MTandHJ/roboc/models/base.py official repository unverified MIT (permissive) · 5732e16eed21a0a5 · report
get_num_classes MTandHJ/roboc/src/loadopts.py official repository unverified MIT (permissive) · a0fcd7383413cd2a · report
load_loss_func MTandHJ/roboc/src/loadopts.py official repository unverified MIT (permissive) · e2723183c9740a56 · report
load_model MTandHJ/roboc/src/loadopts.py official repository unverified MIT (permissive) · f3b694c6c2fed2b1 · report

Tasks

Adversarial AttackAdversarial Robustness

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Adversarial Attack CIFAR-10 Xu et al. Attack: AutoAttack 44.150 #1 of 6 Archive leaderboard report
Adversarial Attack CIFAR-10 Xu et al. Attack: DeepFool 51.310 #1 of 6 Archive leaderboard report
Adversarial Attack CIFAR-10 Xu et al. Attack: PGD20 78.680 #1 of 6 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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