Imperceptible Adversarial Perturbation

Imperceptible adversarial perturbations are subtly altered inputs designed to mislead deep learning models while remaining undetectable to humans. Current research focuses on generating these perturbations for various modalities (images, audio, video) using techniques like generative adversarial networks, diffusion models, and evolutionary algorithms, often targeting specific model architectures or aiming for improved transferability across different models. This field is crucial for assessing and improving the robustness of AI systems in security-sensitive applications, impacting areas like facial recognition, image classification, and speech recognition, where vulnerabilities to such attacks pose significant risks.

Papers

September 20, 2024

ID-Guard: A Universal Framework for Combating Facial Manipulation via Breaking Identification
Zuomin Qu, Wei Lu, Xiangyang Luo, Qian Wang, Xiaochun Cao
Person Identification Universal Model Imperceptible Adversarial Perturbation Face Manipulation

July 29, 2024

Adversarial Robustness in RGB-Skeleton Action Recognition: Leveraging Attention Modality Reweighter
Chao Liu, Xin Liu, Zitong Yu, Yonghong Hou, Huanjing Yue, Jingyu Yang
Adversarial Example Adversarial Robustness Action Recognition Multimodal Model Skeleton Based Action Recognition Multi Modal Learning Attention Weight Imperceptible Adversarial Perturbation

April 30, 2024

Causal Perception Inspired Representation Learning for Trustworthy Image Quality Assessment
Lei Wang, Desen Yuan
Adversarial Perturbation Image Quality Assessment Causal Representation Learning Imperceptible Adversarial Perturbation Causal Machine

April 4, 2024

Meta Invariance Defense Towards Generalizable Robustness to Unknown Adversarial Attacks
Lei Zhang, Yuhang Zhou, Yi Yang, Xinbo Gao
Adversarial Attack Adversarial Robustness Adversarial DEfense Imperceptible Adversarial Perturbation

December 18, 2023

Adv-Diffusion: Imperceptible Adversarial Face Identity Attack via Latent Diffusion Model
Decheng Liu, Xijun Wang, Chunlei Peng, Nannan Wang, Ruiming Hu, Xinbo Gao
Adversarial Attack Image Generation Latent Diffusion Model Imperceptible Adversarial Perturbation Diffusion Based Framework Adversarial Face Realistic Adversarial

November 30, 2023

Can Protective Perturbation Safeguard Personal Data from Being Exploited by Stable Diffusion?
Zhengyue Zhao, Jinhao Duan, Kaidi Xu, Chenan Wang, Rui Zhang, Zidong Du, Qi Guo, Xing Hu
Stable Diffusion Privacy Preservation Privacy Preserving Image Stable Diffusion Model Face Forgery Imperceptible Adversarial Perturbation

August 2, 2023

Inaudible Adversarial Perturbation: Manipulating the Recognition of User Speech in Real Time
Xinfeng Li, Chen Yan, Xuancun Lu, Zihan Zeng, Xiaoyu Ji, Wenyuan Xu
Automatic Speech Recognition Adversarial Example Real Time Recognition Rate Imperceptible Adversarial Perturbation User Utterance

July 27, 2023

When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-$k$ Multi-Label Learning
Yuchen Sun, Qianqian Xu, Zitai Wang, Qingming Huang
Adversarial Attack Adversarial Perturbation Adversarial Learning Multi Label New Measure Imperceptible Adversarial Perturbation

July 2, 2023

Query-Efficient Decision-based Black-Box Patch Attack
Zhaoyu Chen, Bo Li, Shuang Wu, Shouhong Ding, Wenqiang Zhang
Imperceptible Adversarial Perturbation Patch Attack

June 25, 2023

A Spectral Perspective towards Understanding and Improving Adversarial Robustness
Binxiao Huang, Rui Lin, Chaofan Tao, Ngai Wong
Native Robustness Adversarial Training Adversarial Robustness Human Understanding Adversarial Input Better Robustness Imperceptible Adversarial Perturbation Spectral Representation

June 5, 2023

Enhance Diffusion to Improve Robust Generalization
Jianhui Sun, Sanchit Sinha, Aidong Zhang
Adversarial Training Robust Generalization Imperceptible Adversarial Perturbation

May 15, 2023

Attacking Perceptual Similarity Metrics
Abhijay Ghildyal, Feng Liu
Adversarial Attack Adversarial Perturbation Imperceptible Adversarial Perturbation Perceptual Similarity Perceptual Similarity Metric

March 29, 2023

Latent Feature Relation Consistency for Adversarial Robustness
Xingbin Liu, Huafeng Kuang, Hong Liu, Xianming Lin, Yongjian Wu, Rongrong Ji
Deep Neural Network Adversarial Example Adversarial Robustness DNN Framework Latent Feature Imperceptible Adversarial Perturbation Latent Consistency Model

February 13, 2023

Raising the Cost of Malicious AI-Powered Image Editing
Hadi Salman, Alaa Khaddaj, Guillaume Leclerc, Andrew Ilyas, Aleksander Madry
Diffusion Model Hidden CoST Vaccine Related Imperceptible Adversarial Perturbation Large Diffusion Model

February 7, 2023

Toward Face Biometric De-identification using Adversarial Examples
Mahdi Ghafourian, Julian Fierrez, Luis Felipe Gomez, Ruben Vera-Rodriguez, Aythami Morales, Zohra Rezgui, Raymond Veldhuis
Adversarial Example Face Recognition Adversarial Method Imperceptible Adversarial Perturbation Face De Identification

January 25, 2023

A Data-Centric Approach for Improving Adversarial Training Through the Lens of Out-of-Distribution Detection
Mohammad Azizmalayeri, Arman Zarei, Alireza Isavand, Mohammad Taghi Manzuri, Mohammad Hossein Rohban
Adversarial Training Distribution Detection Camera Lens Data Centric Imperceptible Adversarial Perturbation Training Time Attack Softmax Probability

December 24, 2022

Frequency Regularization for Improving Adversarial Robustness
Binxiao Huang, Chaofan Tao, Rui Lin, Ngai Wong
Adversarial Training Adversarial Robustness Adversarial Input Imperceptible Adversarial Perturbation Robustness Problem Frequency Regularization

December 18, 2022

Estimating the Adversarial Robustness of Attributions in Text with Transformers
Adam Ivankay, Mattia Rigotti, Ivan Girardi, Chiara Marchiori, Pascal Frossard
Transformer Megatron Decepticons Adversarial Robustness Text Modality Adversarial Perturbation Imperceptible Adversarial Perturbation Robust Explanation Attribution Robustness Adversarial Change

December 14, 2022

Understanding Zero-Shot Adversarial Robustness for Large-Scale Models
Chengzhi Mao, Scott Geng, Junfeng Yang, Xin Wang, Carl Vondrick
Adversarial Robustness Large Scale Model Imperceptible Adversarial Perturbation Zero Shot Adversarial Robustness

November 17, 2022

Privacy against Real-Time Speech Emotion Detection via Acoustic Adversarial Evasion of Machine Learning
Brian Testa, Yi Xiao, Harshit Sharma, Avery Gump, Asif Salekin
Machine Learning Speech Emotion Recognition Privacy Policy Voice Assistant Imperceptible Adversarial Perturbation Speech Transcription Adversarial Audio Smart Speaker

Imperceptible Adversarial Perturbation

Papers

ID-Guard: A Universal Framework for Combating Facial Manipulation via Breaking Identification

Adversarial Robustness in RGB-Skeleton Action Recognition: Leveraging Attention Modality Reweighter

Causal Perception Inspired Representation Learning for Trustworthy Image Quality Assessment

Meta Invariance Defense Towards Generalizable Robustness to Unknown Adversarial Attacks

Adv-Diffusion: Imperceptible Adversarial Face Identity Attack via Latent Diffusion Model

Can Protective Perturbation Safeguard Personal Data from Being Exploited by Stable Diffusion?

Inaudible Adversarial Perturbation: Manipulating the Recognition of User Speech in Real Time

When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-$k$ Multi-Label Learning

Query-Efficient Decision-based Black-Box Patch Attack

A Spectral Perspective towards Understanding and Improving Adversarial Robustness

Enhance Diffusion to Improve Robust Generalization

Attacking Perceptual Similarity Metrics

Latent Feature Relation Consistency for Adversarial Robustness

Raising the Cost of Malicious AI-Powered Image Editing

Toward Face Biometric De-identification using Adversarial Examples

A Data-Centric Approach for Improving Adversarial Training Through the Lens of Out-of-Distribution Detection

Frequency Regularization for Improving Adversarial Robustness

Estimating the Adversarial Robustness of Attributions in Text with Transformers

Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

Privacy against Real-Time Speech Emotion Detection via Acoustic Adversarial Evasion of Machine Learning