# Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training

arXiv: https://arxiv.org/abs/1907.10764 · HTML: https://mlsanity.com/paper/1907.10764/

## Key facts

- Cited by 95 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2019.

## Built on by (most cited first, 30 of 95)

- [Recent Advances in Adversarial Training for Adversarial Robustness](https://mlsanity.com/paper/2102.01356/) (2021)
- [Cross-Entropy Loss Functions: Theoretical Analysis and Applications](https://mlsanity.com/paper/2304.07288/) (2023)
- [CycleResearcher: Improving Automated Research via Automated Review](https://mlsanity.com/paper/2411.00816/) (2024)
- [Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity](https://mlsanity.com/paper/2203.05151/) (2022)
- [SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness](https://mlsanity.com/paper/2207.12391/) (2022)
- [How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses](https://mlsanity.com/paper/2305.10862/) (2023)
- [Practical Evaluation of Adversarial Robustness via Adaptive Auto Attack](https://mlsanity.com/paper/2203.05154/) (2022)
- [A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies](https://mlsanity.com/paper/2203.14046/) (2022)
- [Enhancing Adversarial Training with Second-Order Statistics of Weights](https://mlsanity.com/paper/2203.06020/) (2022)
- [Few-shot Backdoor Defense Using Shapley Estimation](https://mlsanity.com/paper/2112.14889/) (2021)
- [A Unified Wasserstein Distributional Robustness Framework for Adversarial Training](https://mlsanity.com/paper/2202.13437/) (2022)
- [DISCO: Adversarial Defense with Local Implicit Functions](https://mlsanity.com/paper/2212.05630/) (2022)
- [An Overview of Compressible and Learnable Image Transformation with Secret Key and Its Applications](https://mlsanity.com/paper/2201.11006/) (2022)
- [AdvRush: Searching for Adversarially Robust Neural Architectures](https://mlsanity.com/paper/2108.01289/) (2021)
- [Randomized Adversarial Training via Taylor Expansion](https://mlsanity.com/paper/2303.10653/) (2023)
- [Combating Adversaries with Anti-Adversaries](https://mlsanity.com/paper/2103.14347/) (2021)
- [TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers](https://mlsanity.com/paper/2210.07562/) (2022)
- [FuRL: Visual-Language Models as Fuzzy Rewards for Reinforcement Learning](https://mlsanity.com/paper/2406.00645/) (2024)
- [LAFEAT: Piercing Through Adversarial Defenses with Latent Features](https://mlsanity.com/paper/2104.09284/) (2021)
- [Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning](https://mlsanity.com/paper/2308.02533/) (2023)
- [Automated Discovery of Adaptive Attacks on Adversarial Defenses](https://mlsanity.com/paper/2102.11860/) (2021)
- [Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Apps](https://mlsanity.com/paper/2101.04401/) (2021)
- [Predify: Augmenting deep neural networks with brain-inspired predictive coding dynamics](https://mlsanity.com/paper/2106.02749/) (2021)
- [Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing](https://mlsanity.com/paper/2301.12554/) (2023)
- [Adversarial Examples can be Effective Data Augmentation for Unsupervised Machine Learning](https://mlsanity.com/paper/2103.01895/) (2021)
- [Adversarial Reinforced Instruction Attacker for Robust Vision-Language Navigation](https://mlsanity.com/paper/2107.11252/) (2021)
- [My Face My Choice: Privacy Enhancing Deepfakes for Social Media Anonymization](https://mlsanity.com/paper/2211.01361/) (2022)
- [Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection](https://mlsanity.com/paper/2302.03857/) (2023)
- [Improving Robustness by Enhancing Weak Subnets](https://mlsanity.com/paper/2201.12765/) (2022)
- [Increasing Confidence in Adversarial Robustness Evaluations](https://mlsanity.com/paper/2206.13991/) (2022)

## Abstract (arXiv)

We introduce a feature scattering-based adversarial training approach for improving model robustness against adversarial attacks. Conventional adversarial training approaches leverage a supervised scheme (either targeted or non-targeted) in generating attacks for training, which typically suffer from issues such as label leaking as noted in recent works. Differently, the proposed approach generates adversarial images for training through feature scattering in the latent space, which is unsupervised in nature and avoids label leaking. More importantly, this new approach generates perturbed images in a collaborative fashion, taking the inter-sample relationships into consideration. We conduct analysis on model robustness and demonstrate the effectiveness of the proposed approach through extensively experiments on different datasets compared with state-of-the-art approaches.