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Adversarial Vision Challenge

2018-08-06 · Wieland Brendel, Jonas Rauber, Alexey Kurakin, Nicolas Papernot, Behar Veliqi, Marcel Salathé, Sharada P. Mohanty, Matthias Bethge

The NIPS 2018 Adversarial Vision Challenge is a competition to facilitate measurable progress towards robust machine vision models and more generally applicable adversarial attacks. This document is an updated version of our competition proposal that was accepted in the competition track of 32nd Conference on Neural Information Processing Systems (NIPS 2018).

📄 PDF Abstract BibTeX arXiv:1808.01976

Code (2)

bethgelab/adversarial-vision-challenge

paperblack/AVC-18

Navigating the Trade-off: A Synthesis of Defensive Strategies for Zero-Shot Adversarial Robustness in Vision-Language Models

2025-08-07 · Zane Xu, Jason Sun arxiv

This report synthesizes eight seminal papers on the zero-shot adversarial robustness of vision-language models (VLMs) like CLIP. A central challenge in this domain is the inherent trade-off between enhancing adversarial …

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Evaluation of Momentum Diverse Input Iterative Fast Gradient Sign Method (M-DI2-FGSM) Based Attack Method on MCS 2018 Adversarial Attacks on Black Box Face Recognition System

2018-06-23 · Md Ashraful Alam Milton

The convolutional neural network is the crucial tool for the recent success of deep learning based methods on various computer vision tasks like classification, segmentation, and detection. Convolutional neural networks …

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Adversarial Machine Learning for Cybersecurity and Computer Vision: Current Developments and Challenges

2021-06-30 · Bowei Xi

We provide a comprehensive overview of adversarial machine learning focusing on two application domains, i.e., cybersecurity and computer vision. Research in adversarial machine learning addresses a significant threat to…

BIG-bench Machine Learning

A Prompt Array Keeps the Bias Away: Debiasing Vision-Language Models with Adversarial Learning

2022-03-22 · Hugo Berg, Siobhan Mackenzie Hall, Yash Bhalgat, Wonsuk Yang 외

Vision-language models can encode societal biases and stereotypes, but there are challenges to measuring and mitigating these multimodal harms due to lacking measurement robustness and feature degradation. To address the…

Adversarial Defense in Vision-Language Models: An Overview

2026-01-18 · Xiaowei Fu, Lei Zhang arxiv

The widespread use of Vision Language Models (VLMs, e.g. CLIP) has raised concerns about their vulnerability to sophisticated and imperceptible adversarial attacks. These attacks could compromise model performance and sy…

Test-time Adaptation Adversarial Defense