Defending against Adversarial Attack towards Deep Neural Networks via Collaborative Multi-task Training

14 March 2018

Papers citing "Defending against Adversarial Attack towards Deep Neural Networks via Collaborative Multi-task Training"

8 / 8 papers shown

Title
Adversarial Examples Detection with Enhanced Image Difference Features based on Local Histogram Equalization Z. Yin Shaowei Zhu Han Su Jianteng Peng Wanli Lyu Bin Luo AAML 31 2 0 08 May 2023
SecureSense: Defending Adversarial Attack for Secure Device-Free Human Activity Recognition Jianfei Yang Han Zou Lihua Xie AAML HAI 30 20 0 04 Apr 2022
Detecting Adversarial Perturbations in Multi-Task Perception Marvin Klingner V. Kumar S. Yogamani Andreas Bär Tim Fingscheidt AAML 32 15 0 02 Mar 2022
Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View Di Jin Elena Sergeeva W. Weng Geeticka Chauhan Peter Szolovits OOD 39 55 0 05 Dec 2021
Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions I. Alsmadi Kashif Ahmad Mahmoud Nazzal Firoj Alam Ala I. Al-Fuqaha Abdallah Khreishah A. Algosaibi AAML 32 16 0 26 Oct 2021
Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspective G. R. Machado Eugênio Silva R. Goldschmidt AAML 33 156 0 08 Sep 2020
Stance Detection Benchmark: How Robust Is Your Stance Detection? Benjamin Schiller Johannes Daxenberger Iryna Gurevych 16 95 0 06 Jan 2020
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 308 5,842 0 08 Jul 2016