Adversarial Attack
Adversarial attacks aim to deceive machine learning models by subtly altering input data, causing misclassifications or other erroneous outputs. Current research focuses on developing more robust models and detection methods, exploring various attack strategies across different model architectures (including vision transformers, recurrent neural networks, and graph neural networks) and data types (images, text, signals, and tabular data). Understanding and mitigating these attacks is crucial for ensuring the reliability and security of AI systems in diverse applications, from autonomous vehicles to medical diagnosis and cybersecurity.
Papers
One Noise to Rule Them All: Multi-View Adversarial Attacks with Universal Perturbation
Mehmet Ergezer, Phat Duong, Christian Green, Tommy Nguyen, Abdurrahman Zeybey
READ: Improving Relation Extraction from an ADversarial Perspective
Dawei Li, William Hogan, Jingbo Shang
Red-Teaming Segment Anything Model
Krzysztof Jankowski, Bartlomiej Sobieski, Mateusz Kwiatkowski, Jakub Szulc, Michal Janik, Hubert Baniecki, Przemyslaw Biecek
Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial Attack
Ying Zhou, Ben He, Le Sun
Defense without Forgetting: Continual Adversarial Defense with Anisotropic & Isotropic Pseudo Replay
Yuhang Zhou, Zhongyun Hua
ADVREPAIR:Provable Repair of Adversarial Attack
Zhiming Chi, Jianan Ma, Pengfei Yang, Cheng-Chao Huang, Renjue Li, Xiaowei Huang, Lijun Zhang
On Inherent Adversarial Robustness of Active Vision Systems
Amitangshu Mukherjee, Timur Ibrayev, Kaushik Roy
Deepfake Sentry: Harnessing Ensemble Intelligence for Resilient Detection and Generalisation
Liviu-Daniel Ştefan, Dan-Cristian Stanciu, Mihai Dogariu, Mihai Gabriel Constantin, Andrei Cosmin Jitaru, Bogdan Ionescu
Robustness and Visual Explanation for Black Box Image, Video, and ECG Signal Classification with Reinforcement Learning
Soumyendu Sarkar, Ashwin Ramesh Babu, Sajad Mousavi, Vineet Gundecha, Avisek Naug, Sahand Ghorbanpour
SemRoDe: Macro Adversarial Training to Learn Representations That are Robust to Word-Level Attacks
Brian Formento, Wenjie Feng, Chuan Sheng Foo, Luu Anh Tuan, See-Kiong Ng
Uncertainty-Aware SAR ATR: Defending Against Adversarial Attacks via Bayesian Neural Networks
Tian Ye, Rajgopal Kannan, Viktor Prasanna, Carl Busart