# A Self-supervised Approach for Adversarial Robustness

arXiv: https://arxiv.org/abs/2006.04924 · HTML: https://mlsanity.com/paper/2006.04924/

## Key facts

- Cited by 180 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2020.

## Built on by (most cited first, 30 of 180)

- [Enhancing the Transferability of Adversarial Attacks through Variance Tuning](https://mlsanity.com/paper/2103.15571/) (2021)
- [Better Diffusion Models Further Improve Adversarial Training](https://mlsanity.com/paper/2302.04638/) (2023)
- [Admix: Enhancing the Transferability of Adversarial Attacks](https://mlsanity.com/paper/2102.00436/) (2021)
- [Frequency Domain Model Augmentation for Adversarial Attack](https://mlsanity.com/paper/2207.05382/) (2022)
- [Advances in adversarial attacks and defenses in computer vision: A survey](https://mlsanity.com/paper/2108.00401/) (2021)
- [AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learning](https://mlsanity.com/paper/2308.07026/) (2023)
- [Diffusion Models for Imperceptible and Transferable Adversarial Attack](https://mlsanity.com/paper/2305.08192/) (2023)
- [Rethinking Model Ensemble in Transfer-based Adversarial Attacks](https://mlsanity.com/paper/2303.09105/) (2023)
- [Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferability](https://mlsanity.com/paper/2111.10752/) (2021)
- [Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation](https://mlsanity.com/paper/2210.05968/) (2022)
- [VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models](https://mlsanity.com/paper/2310.04655/) (2023)
- [On Generating Transferable Targeted Perturbations](https://mlsanity.com/paper/2103.14641/) (2021)
- [Boosting Adversarial Transferability by Block Shuffle and Rotation](https://mlsanity.com/paper/2308.10299/) (2023)
- [Structure Invariant Transformation for better Adversarial Transferability](https://mlsanity.com/paper/2309.14700/) (2023)
- [Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training](https://mlsanity.com/paper/2111.01118/) (2021)
- [Boosting Adversarial Transferability by Achieving Flat Local Maxima](https://mlsanity.com/paper/2306.05225/) (2023)
- [Online Adversarial Purification based on Self-Supervision](https://mlsanity.com/paper/2101.09387/) (2021)
- [AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Models](https://mlsanity.com/paper/2307.12499/) (2023)
- [Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains](https://mlsanity.com/paper/2201.11528/) (2022)
- [An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial Transferability](https://mlsanity.com/paper/2308.02897/) (2023)
- [One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models](https://mlsanity.com/paper/2406.05491/) (2024)
- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [Natural Color Fool: Towards Boosting Black-box Unrestricted Attacks](https://mlsanity.com/paper/2210.02041/) (2022)
- [Diversifying the High-level Features for better Adversarial Transferability](https://mlsanity.com/paper/2304.10136/) (2023)
- [Downstream-agnostic Adversarial Examples](https://mlsanity.com/paper/2307.12280/) (2023)
- [DarkSAM: Fooling Segment Anything Model to Segment Nothing](https://mlsanity.com/paper/2409.17874/) (2024)
- [Unified Detection of Digital and Physical Face Attacks](https://mlsanity.com/paper/2104.02156/) (2021)
- [Boosting the Transferability of Adversarial Attacks with Global Momentum Initialization](https://mlsanity.com/paper/2211.11236/) (2022)
- [Improving Adversarial Transferability via Intermediate-level Perturbation Decay](https://mlsanity.com/paper/2304.13410/) (2023)
- [Rethinking the Backward Propagation for Adversarial Transferability](https://mlsanity.com/paper/2306.12685/) (2023)

## Abstract (arXiv)

Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems e.g., for classification, segmentation and object detection. The vulnerability of DNNs against such attacks can prove a major roadblock towards their real-world deployment. Transferability of adversarial examples demand generalizable defenses that can provide cross-task protection. Adversarial training that enhances robustness by modifying target model's parameters lacks such generalizability. On the other hand, different input processing based defenses fall short in the face of continuously evolving attacks. In this paper, we take the first step to combine the benefits of both approaches and propose a self-supervised adversarial training mechanism in the input space. By design, our defense is a generalizable approach and provides significant robustness against the unseen adversarial attacks (eg by reducing the success rate of translation-invariant ensemble attack from 82.6% to 31.9% in comparison to previous state-of-the-art). It can be deployed as a plug-and-play solution to protect a variety of vision systems, as we demonstrate for the case of classification, segmentation and detection. Code is available at: smallurlhttps://github.com/Muzammal-Naseer/NRP.