Adversarial Noise

Adversarial noise refers to carefully crafted perturbations added to data to mislead machine learning models, primarily deep neural networks. Current research focuses on detecting and mitigating these attacks across various modalities (images, audio, text), employing techniques like generative models (diffusion models), variational sparsification, and biologically-inspired feature extraction to enhance robustness. This field is crucial for ensuring the reliability and security of AI systems in diverse applications, from facial recognition and autonomous driving to medical image analysis and speech recognition, where vulnerabilities to adversarial manipulation can have significant consequences.

Papers

August 9, 2024

XNN: Paradigm Shift in Mitigating Identity Leakage within Cloud-Enabled Deep Learning
Kaixin Liu, Huixin Xiong, Bingyu Duan, Zexuan Cheng, Xinyu Zhou, Wanqian Zhang, Xiangyu Zhang
Adversarial Training Adversarial Noise Paradigm Shift Cloud Based

July 14, 2024

Disrupting Diffusion-based Inpainters with Semantic Digression
Geonho Son, Juhun Lee, Simon S. Woo
Text to Image Diffusion Model Deep Fake Adversarial Noise Disruption Prediction

July 2, 2024

Light-weight Fine-tuning Method for Defending Adversarial Noise in Pre-trained Medical Vision-Language Models
Xu Han, Linghao Jin, Xuezhe Ma, Xiaofeng Liu
Adversarial Attack Vision Language Adversarial Noise Medical Vision Language Pre Training

June 16, 2024

Improving Adversarial Robustness via Decoupled Visual Representation Masking
Decheng Liu, Tao Chen, Chunlei Peng, Nannan Wang, Ruimin Hu, Xinbo Gao
Adversarial Robustness Adversarial DEfense Adversarial Noise Feature Masking

June 7, 2024

Probabilistic Perspectives on Error Minimization in Adversarial Reinforcement Learning
Roman Belaire, Arunesh Sinha, Pradeep Varakantham
Adversarial Attack Deep Reinforcement Learning Adversarial Perturbation Bayesian Perspective Adversarial Noise Adversarial Reinforcement Learning Error Minimizing Noise

June 5, 2024

VQUNet: Vector Quantization U-Net for Defending Adversarial Atacks by Regularizing Unwanted Noise
Zhixun He, Mukesh Singhal
Adversarial Attack Industrial Disturbing Noise U Net Adversarial Noise Adversarial Prediction

May 30, 2024

BAN: Detecting Backdoors Activated by Adversarial Neuron Noise
Xiaoyun Xu, Zhuoran Liu, Stefanos Koffas, Shujian Yu, Stjepan Picek
Backdoor Defense Adversarial Noise Backdoor Detection Policy Violation Hidden Backdoor Backdoor Inversion

May 25, 2024

Detecting Adversarial Data via Perturbation Forgery
Qian Wang, Chen Li, Yuchen Luo, Hefei Ling, Ping Li, Jiazhong Chen, Shijuan Huang, Ning Yu
Adversarial Attack Adversarial Noise Adversarial Detection Adversarial Data Gradient Based Adversarial Specific Forgery

April 17, 2024

Online Algorithms with Limited Data Retention
Nicole Immorlica, Brendan Lucier, Markus Mobius, James Siderius
Online Algorithm Adversarial Noise Online Learning Algorithm Information Retention Retention Index

April 16, 2024

Efficient Generation of Targeted and Transferable Adversarial Examples for Vision-Language Models Via Diffusion Models
Qi Guo, Shanmin Pang, Xiaojun Jia, Yang Liu, Qing Guo
Diffusion Model Adversarial Example Visual Language Model Adversarial Noise Adversarial Defense Unrestricted Adversarial Transfer Risk

April 12, 2024

Struggle with Adversarial Defense? Try Diffusion
Yujie Li, Yanbin Wang, Haitao Xu, Bin Liu, Jianguo Sun, Zhenhao Guo, Wenrui Ma
Adversarial Attack Adversarial Robustness Diffusion Explainer Adversarial DEfense Adversarial Noise Diffusion Classifier

February 16, 2024

Resilience of the quadratic Littlewood-Offord problem
Elad Aigner-Horev, Daniel Rozenberg, Roi Weiss
High Dimensional Data Tiny Refinement Elicit Resilience Adversarial Noise Convex Quadratic Polynomial Chaos

February 9, 2024

Anomaly Unveiled: Securing Image Classification against Adversarial Patch Attacks
Nandish Chattopadhyay, Amira Guesmi, Muhammad Shafique
Unlabeled Anomaly Adversarial Patch Adversarial Noise Privacy Preserving Image Adversarial Patch Attack Adversarial Feature

February 6, 2024

FoolSDEdit: Deceptively Steering Your Edits Towards Targeted Attribute-aware Distribution
Qi Zhou, Dongxia Wang, Tianlin Li, Zhihong Xu, Yang Liu, Kui Ren, Wenhai Wang, Qing Guo
Adversarial Attack Adversarial Noise Deceptive Diffusion Generative Attack Blur Attack Attribute Distribution Joint Adversarial

February 1, 2024

Comparing Spectral Bias and Robustness For Two-Layer Neural Networks: SGD vs Adaptive Random Fourier Features
Aku Kammonen, Lisi Liang, Anamika Pandey, Raúl Tempone
Native Robustness Stochastic Gradient Descent Two Layer Neural Network Adversarial Noise Spectral Bias Random Fourier Feature

January 17, 2024

Attack and Reset for Unlearning: Exploiting Adversarial Noise toward Machine Unlearning through Parameter Re-initialization
Yoonhwa Jung, Ikhyun Cho, Shun-Hsiang Hsu, Julia Hockenmaier
Deep Learning Model Machine Unlearning Unlearning Framework Harmful Unlearning Adversarial Noise Global Reset Feature Parameter Re Initialization

January 14, 2024

Left-right Discrepancy for Adversarial Attack on Stereo Networks
Pengfei Wang, Xiaofei Hui, Beijia Lu, Nimrod Lilith, Jun Liu, Sameer Alam
Adversarial Attack Adversarial Noise Task Discrepancy Stereo Network

December 14, 2023

December 13, 2023

SimAC: A Simple Anti-Customization Method for Protecting Face Privacy against Text-to-Image Synthesis of Diffusion Models
Feifei Wang, Zhentao Tan, Tianyi Wei, Yue Wu, Qidong Huang
Diffusion Model Text to Image Synthesis Adversarial Noise Privacy Protection Customization Method

Adversarial Noise

Papers

XNN: Paradigm Shift in Mitigating Identity Leakage within Cloud-Enabled Deep Learning

Disrupting Diffusion-based Inpainters with Semantic Digression

Light-weight Fine-tuning Method for Defending Adversarial Noise in Pre-trained Medical Vision-Language Models

Improving Adversarial Robustness via Decoupled Visual Representation Masking

Probabilistic Perspectives on Error Minimization in Adversarial Reinforcement Learning

VQUNet: Vector Quantization U-Net for Defending Adversarial Atacks by Regularizing Unwanted Noise

BAN: Detecting Backdoors Activated by Adversarial Neuron Noise

Detecting Adversarial Data via Perturbation Forgery

Online Algorithms with Limited Data Retention

Efficient Generation of Targeted and Transferable Adversarial Examples for Vision-Language Models Via Diffusion Models

Struggle with Adversarial Defense? Try Diffusion

Resilience of the quadratic Littlewood-Offord problem

Anomaly Unveiled: Securing Image Classification against Adversarial Patch Attacks

FoolSDEdit: Deceptively Steering Your Edits Towards Targeted Attribute-aware Distribution

Comparing Spectral Bias and Robustness For Two-Layer Neural Networks: SGD vs Adaptive Random Fourier Features

Attack and Reset for Unlearning: Exploiting Adversarial Noise toward Machine Unlearning through Parameter Re-initialization

Left-right Discrepancy for Adversarial Attack on Stereo Networks

Detection and Defense of Unlearnable Examples

Scalable Ensemble-based Detection Method against Adversarial Attacks for speaker verification

SimAC: A Simple Anti-Customization Method for Protecting Face Privacy against Text-to-Image Synthesis of Diffusion Models