# Combating Adversaries with Anti-Adversaries

arXiv: https://arxiv.org/abs/2103.14347 · HTML: https://mlsanity.com/paper/2103.14347/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 52 references that are in the map.
- First posted on arXiv 26 Mar 2021.

## Builds on (most cited first, 30 of 35)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Theoretically Principled Trade-off between Robustness and Accuracy](https://mlsanity.com/paper/1901.08573/) (2019)
- [Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks](https://mlsanity.com/paper/2003.01690/) (2020)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples](https://mlsanity.com/paper/1802.00420/) (2018)
- [Practical Black-Box Attacks against Machine Learning](https://mlsanity.com/paper/1602.02697/) (2016)
- [Robustness May Be at Odds with Accuracy](https://mlsanity.com/paper/1805.12152/) (2018)
- [Square Attack: a query-efficient black-box adversarial attack via random search](https://mlsanity.com/paper/1912.00049/) (2019)
- [Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks](https://mlsanity.com/paper/1511.04508/) (2015)
- [OptNet: Differentiable Optimization as a Layer in Neural Networks](https://mlsanity.com/paper/1703.00443/) (2017)
- [On Adaptive Attacks to Adversarial Example Defenses](https://mlsanity.com/paper/2002.08347/) (2020)
- [Black-box Adversarial Attacks with Limited Queries and Information](https://mlsanity.com/paper/1804.08598/) (2018)
- [Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models](https://mlsanity.com/paper/1712.04248/) (2017)
- [On Evaluating Adversarial Robustness](https://mlsanity.com/paper/1902.06705/) (2019)
- [Unlabeled Data Improves Adversarial Robustness](https://mlsanity.com/paper/1905.13736/) (2019)
- [Mitigating Adversarial Effects Through Randomization](https://mlsanity.com/paper/1711.01991/) (2017)
- [Using Pre-Training Can Improve Model Robustness and Uncertainty](https://mlsanity.com/paper/1901.09960/) (2019)
- [Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack](https://mlsanity.com/paper/1907.02044/) (2019)
- [Feature Denoising for Improving Adversarial Robustness](https://mlsanity.com/paper/1812.03411/) (2018)
- [Parseval Networks: Improving Robustness to Adversarial Examples](https://mlsanity.com/paper/1704.08847/) (2017)
- [Simple Black-box Adversarial Attacks](https://mlsanity.com/paper/1905.07121/) (2019)
- [Adversarial Risk and the Dangers of Evaluating Against Weak Attacks](https://mlsanity.com/paper/1802.05666/) (2018)
- [Towards Robust Neural Networks via Random Self-ensemble](https://mlsanity.com/paper/1712.00673/) (2017)
- [Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors](https://mlsanity.com/paper/1807.07978/) (2018)

## Built on by (most cited first, 27 of 27)

- [A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts](https://mlsanity.com/paper/2303.15361/) (2023)
- [Evaluating the Adversarial Robustness of Adaptive Test-time Defenses](https://mlsanity.com/paper/2202.13711/) (2022)
- [Visual Prompting for Adversarial Robustness](https://mlsanity.com/paper/2210.06284/) (2022)
- [DISCO: Adversarial Defense with Local Implicit Functions](https://mlsanity.com/paper/2212.05630/) (2022)
- [CLIP is Strong Enough to Fight Back: Test-time Counterattacks towards Zero-shot Adversarial Robustness of CLIP](https://mlsanity.com/paper/2503.03613/) (2025)
- [Evaluation of Test-Time Adaptation Under Computational Time Constraints](https://mlsanity.com/paper/2304.04795/) (2023)
- [AutoAdvExBench: Benchmarking autonomous exploitation of adversarial example defenses](https://mlsanity.com/paper/2503.01811/) (2025)
- [Enhancing CLIP Robustness via Cross-Modality Alignment](https://mlsanity.com/paper/2510.24038/) (2025)
- [Defenses in Adversarial Machine Learning: A Survey](https://mlsanity.com/paper/2312.08890/) (2023)
- [Adaptive Randomized Smoothing: Certified Adversarial Robustness for Multi-Step Defences](https://mlsanity.com/paper/2406.10427/) (2024)
- [Robust Feature Inference: A Test-time Defense Strategy using Spectral Projections](https://mlsanity.com/paper/2307.11672/) (2023)
- [Interpretability-Guided Test-Time Adversarial Defense](https://mlsanity.com/paper/2409.15190/) (2024)
- [Self-Calibrated Consistency can Fight Back for Adversarial Robustness in Vision-Language Models](https://mlsanity.com/paper/2510.22785/) (2025)
- [Attacking Adversarial Defences by Smoothing the Loss Landscape](https://mlsanity.com/paper/2208.00862/) (2022)
- [Improving Adversarial Robustness of DEQs with Explicit Regulations Along the Neural Dynamics](https://mlsanity.com/paper/2306.01435/) (2023)
- [Contrastive Spectral Rectification: Test-Time Defense towards Zero-shot Adversarial Robustness of CLIP](https://mlsanity.com/paper/2601.19210/) (2026)
- [Unifying Gradients to Improve Real-world Robustness for Deep Networks](https://mlsanity.com/paper/2208.06228/) (2022)
- [Test-time adversarial detection and robustness for localizing humans using ultra wide band channel impulse responses](https://mlsanity.com/paper/2211.05854/) (2022)
- [Test-time Adversarial Defense with Opposite Adversarial Path and High Attack Time Cost](https://mlsanity.com/paper/2410.16805/) (2024)
- [Adversarial Examples in Environment Perception for Automated Driving (Review)](https://mlsanity.com/paper/2504.08414/) (2025)
- [ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking](https://mlsanity.com/paper/2509.02182/) (2025)
- [A Provable Energy-Guided Test-Time Defense Boosting Adversarial Robustness of Large Vision-Language Models](https://mlsanity.com/paper/2603.26984/) (2026)
- [Certified vs. Empirical Adversarial Robust-ness via Hybrid Convolutions with Attention Stochasticity](https://mlsanity.com/paper/2605.01519/) (2026)
- [When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness](https://mlsanity.com/paper/2606.06938/) (2026)
- [DataCook: Crafting Anti-Adversarial Examples for Healthcare Data Copyright Protection](https://mlsanity.com/paper/2403.17755/) (2024)
- [Diffusion or Non-Diffusion Adversarial Defenses: Rethinking the Relation between Classifier and Adversarial Purifier](https://mlsanity.com/paper/2501.16904/) (2025)
- [Closed-Loop Bidirectional Prompting for Adversarial Robustness of Vision Language Models](https://mlsanity.com/paper/2605.25922/) (2026)

## Abstract (arXiv)

Deep neural networks are vulnerable to small input perturbations known as adversarial attacks. Inspired by the fact that these adversaries are constructed by iteratively minimizing the confidence of a network for the true class label, we propose the anti-adversary layer, aimed at countering this effect. In particular, our layer generates an input perturbation in the opposite direction of the adversarial one and feeds the classifier a perturbed version of the input. Our approach is training-free and theoretically supported. We verify the effectiveness of our approach by combining our layer with both nominally and robustly trained models and conduct large-scale experiments from black-box to adaptive attacks on CIFAR10, CIFAR100, and ImageNet. Our layer significantly enhances model robustness while coming at no cost on clean accuracy.