# Adversarial Attacks and Defences Competition

arXiv: https://arxiv.org/abs/1804.00097 · HTML: https://mlsanity.com/paper/1804.00097/

## Key facts

- Cited by 96 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2018.

## Built on by (most cited first, 30 of 96)

- [Jailbreaking Black Box Large Language Models in Twenty Queries](https://mlsanity.com/paper/2310.08419/) (2023)
- [Better Diffusion Models Further Improve Adversarial Training](https://mlsanity.com/paper/2302.04638/) (2023)
- [Agreement-on-the-Line: Predicting the Performance of Neural Networks under Distribution Shift](https://mlsanity.com/paper/2206.13089/) (2022)
- [Diffusion Models for Imperceptible and Transferable Adversarial Attack](https://mlsanity.com/paper/2305.08192/) (2023)
- [Improving Adversarial Transferability via Neuron Attribution-Based Attacks](https://mlsanity.com/paper/2204.00008/) (2022)
- [No One Representation to Rule Them All: Overlapping Features of Training Methods](https://mlsanity.com/paper/2110.12899/) (2021)
- [Meta Gradient Adversarial Attack](https://mlsanity.com/paper/2108.04204/) (2021)
- [Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input](https://mlsanity.com/paper/2203.09123/) (2022)
- [Improving the Transferability of Adversarial Samples by Path-Augmented Method](https://mlsanity.com/paper/2303.15735/) (2023)
- [Transferable Adversarial Attack based on Integrated Gradients](https://mlsanity.com/paper/2205.13152/) (2022)
- [Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks](https://mlsanity.com/paper/2107.01809/) (2021)
- [Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses](https://mlsanity.com/paper/2104.01789/) (2021)
- [Overparameterized Linear Regression under Adversarial Attacks](https://mlsanity.com/paper/2204.06274/) (2022)
- [PRoA: A Probabilistic Robustness Assessment against Functional Perturbations](https://mlsanity.com/paper/2207.02036/) (2022)
- [MirrorCheck: Efficient Adversarial Defense for Vision-Language Models](https://mlsanity.com/paper/2406.09250/) (2024)
- [Regularization properties of adversarially-trained linear regression](https://mlsanity.com/paper/2310.10807/) (2023)
- [Realistic Full-Body Anonymization with Surface-Guided GANs](https://mlsanity.com/paper/2201.02193/) (2022)
- [Lifelong Safety Alignment for Language Models](https://mlsanity.com/paper/2505.20259/) (2025)
- [Learning to Learn Transferable Attack](https://mlsanity.com/paper/2112.06658/) (2021)
- [Blackbox Attacks via Surrogate Ensemble Search](https://mlsanity.com/paper/2208.03610/) (2022)
- [Black-box Targeted Adversarial Attack on Segment Anything (SAM)](https://mlsanity.com/paper/2310.10010/) (2023)
- [Robust Reinforcement Learning via Genetic Curriculum](https://mlsanity.com/paper/2202.08393/) (2022)
- [Invariant Learning via Diffusion Dreamed Distribution Shifts](https://mlsanity.com/paper/2211.10370/) (2022)
- [Transferable Adversarial Attacks on Black-Box Vision-Language Models](https://mlsanity.com/paper/2505.01050/) (2025)
- [A Simple and Strong Baseline for Universal Targeted Attacks on Siamese Visual Tracking](https://mlsanity.com/paper/2105.02480/) (2021)
- [AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning](https://mlsanity.com/paper/2107.06501/) (2021)
- [Diverse Generative Perturbations on Attention Space for Transferable Adversarial Attacks](https://mlsanity.com/paper/2208.05650/) (2022)
- [Adversarial Attack with Raindrops](https://mlsanity.com/paper/2302.14267/) (2023)
- [Adversarial alignment: Breaking the trade-off between the strength of an attack and its relevance to human perception](https://mlsanity.com/paper/2306.03229/) (2023)
- [Boosting the Transferability of Adversarial Examples via Local Mixup and Adaptive Step Size](https://mlsanity.com/paper/2401.13205/) (2024)

## Abstract (arXiv)

To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them. In this chapter, we describe the structure and organization of the competition and the solutions developed by several of the top-placing teams.