# Bluff: Interactively Deciphering Adversarial Attacks on Deep Neural Networks

arXiv: https://arxiv.org/abs/2009.02608 · HTML: https://mlsanity.com/paper/2009.02608/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2020.

## Built on by (most cited first, 12 of 12)

- [Visual Analytics For Machine Learning: A Data Perspective Survey](https://mlsanity.com/paper/2307.07712/) (2023)
- [NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networks](https://mlsanity.com/paper/2108.12931/) (2021)
- [VisCUIT: Visual Auditor for Bias in CNN Image Classifier](https://mlsanity.com/paper/2204.05899/) (2022)
- [JailbreakLens: Visual Analysis of Jailbreak Attacks Against Large Language Models](https://mlsanity.com/paper/2404.08793/) (2024)
- [Interactive Analysis of CNN Robustness](https://mlsanity.com/paper/2110.07667/) (2021)
- [Visual Analytics of Neuron Vulnerability to Adversarial Attacks on Convolutional Neural Networks](https://mlsanity.com/paper/2303.02814/) (2023)
- [Concept Evolution in Deep Learning Training: A Unified Interpretation Framework and Discoveries](https://mlsanity.com/paper/2203.16475/) (2022)
- [Panda or not Panda? Understanding Adversarial Attacks with Interactive Visualization](https://mlsanity.com/paper/2311.13656/) (2023)
- [Adversarial Attacks on Machine Learning-Aided Visualizations](https://mlsanity.com/paper/2409.02485/) (2024)
- [SkeletonVis: Interactive Visualization for Understanding Adversarial Attacks on Human Action Recognition Models](https://mlsanity.com/paper/2101.10586/) (2021)
- [ChannelExplorer: Exploring Class Separability Through Activation Channel Visualization](https://mlsanity.com/paper/2505.04647/) (2025)
- [Constructive Interpretability with CoLabel: Corroborative Integration, Complementary Features, and Collaborative Learning](https://mlsanity.com/paper/2205.10011/) (2022)

## Abstract (arXiv)

Deep neural networks (DNNs) are now commonly used in many domains. However, they are vulnerable to adversarial attacks: carefully crafted perturbations on data inputs that can fool a model into making incorrect predictions. Despite significant research on developing DNN attack and defense techniques, people still lack an understanding of how such attacks penetrate a model's internals. We present Bluff, an interactive system for visualizing, characterizing, and deciphering adversarial attacks on vision-based neural networks. Bluff allows people to flexibly visualize and compare the activation pathways for benign and attacked images, revealing mechanisms that adversarial attacks employ to inflict harm on a model. Bluff is open-sourced and runs in modern web browsers.