Ask, Acquire, and Attack: Data-free UAP Generation using Class Impressions

3 August 2018

Papers citing "Ask, Acquire, and Attack: Data-free UAP Generation using Class Impressions"

32 / 32 papers shown

Title
Data-free Universal Adversarial Perturbation with Pseudo-semantic Prior Chanhui Lee Yeonghwan Song Jeany Son AAML 427 0 0 28 Feb 2025
Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Namhyuk Ahn Kiyoon Yoo Wonhyuk Ahn Daesik Kim Seung-Hun Nam AAML WIGM DiffM 190 0 0 16 Dec 2024
One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models Hao Fang Jiawei Kong Wenbo Yu Bin Chen Jiawei Li Hao Wu Ke Xu Ke Xu AAML VLM 131 13 0 08 Jun 2024
Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems Guangjing Wang Ce Zhou Yuanda Wang Bocheng Chen Hanqing Guo Qiben Yan AAML SILM 135 3 0 20 Nov 2023
Learning video embedding space with Natural Language Supervision P. Uppala Abhishek Bamotra S. Priya Vaidehi Joshi CLIP 38 1 0 25 Mar 2023
FG-UAP: Feature-Gathering Universal Adversarial Perturbation Zhixing Ye Xinwen Cheng Xiaolin Huang AAML 108 11 0 27 Sep 2022
Robust Feature-Level Adversaries are Interpretability Tools Stephen Casper Max Nadeau Dylan Hadfield-Menell Gabriel Kreiman AAML 178 28 0 07 Oct 2021
MINIMAL: Mining Models for Data Free Universal Adversarial Triggers Swapnil Parekh Yaman Kumar Singla Somesh Singh Changyou Chen Balaji Krishnamurthy R. Shah AAML 49 3 0 25 Sep 2021
When and How to Fool Explainable Models (and Humans) with Adversarial Examples Jon Vadillo Roberto Santana Jose A. Lozano SILM AAML 95 13 0 05 Jul 2021
Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks Xiao Yang Yinpeng Dong Tianyu Pang Hang Su Jun Zhu AAML 79 39 0 05 Jul 2021
Dominant Patterns: Critical Features Hidden in Deep Neural Networks Zhixing Ye S. Qin Sizhe Chen Xiaolin Huang AAML 57 2 0 31 May 2021
Performance Evaluation of Adversarial Attacks: Discrepancies and Solutions Jing Wu Mingyi Zhou Ce Zhu Yipeng Liu Mehrtash Harandi Li Li AAML 107 11 0 22 Apr 2021
PrivateSNN: Privacy-Preserving Spiking Neural Networks Youngeun Kim Yeshwanth Venkatesha Priyadarshini Panda 69 23 0 07 Apr 2021
Universal Adversarial Training with Class-Wise Perturbations Philipp Benz Chaoning Zhang Adil Karjauv In So Kweon AAML 58 27 0 07 Apr 2021
On Generating Transferable Targeted Perturbations Muzammal Naseer Salman Khan Munawar Hayat Fahad Shahbaz Khan Fatih Porikli AAML 109 75 0 26 Mar 2021
A Survey On Universal Adversarial Attack Chaoning Zhang Philipp Benz Chenguo Lin Adil Karjauv Jing Wu In So Kweon AAML 78 93 0 02 Mar 2021
Effective Universal Unrestricted Adversarial Attacks using a MOE Approach Alina Elena Baia G. D. Bari V. Poggioni AAML 72 8 0 27 Feb 2021
Mining Data Impressions from Deep Models as Substitute for the Unavailable Training Data Gaurav Kumar Nayak Konda Reddy Mopuri Saksham Jain Anirban Chakraborty 68 14 0 15 Jan 2021
On Success and Simplicity: A Second Look at Transferable Targeted Attacks Zhengyu Zhao Zhuoran Liu Martha Larson AAML 167 126 0 21 Dec 2020
Towards Imperceptible Universal Attacks on Texture Recognition Yingpeng Deng Lina Karam AAML 36 1 0 24 Nov 2020
Adversarial Threats to DeepFake Detection: A Practical Perspective Paarth Neekhara Brian Dolhansky Joanna Bitton Cristian Canton Ferrer AAML 61 85 0 19 Nov 2020
Transferable Universal Adversarial Perturbations Using Generative Models Atiyeh Hashemi Andreas Bär S. Mozaffari Tim Fingscheidt AAML 78 17 0 28 Oct 2020
CD-UAP: Class Discriminative Universal Adversarial Perturbation Chaoning Zhang Philipp Benz Tooba Imtiaz In So Kweon AAML 63 61 0 07 Oct 2020
Saliency-driven Class Impressions for Feature Visualization of Deep Neural Networks Sravanti Addepalli Dipesh Tamboli R. Venkatesh Babu Biplab Banerjee FAtt 31 3 0 31 Jul 2020
Understanding Adversarial Examples from the Mutual Influence of Images and Perturbations Chaoning Zhang Philipp Benz Tooba Imtiaz In-So Kweon SSL AAML 83 119 0 13 Jul 2020
Universal Adversarial Perturbations: A Survey Ashutosh Chaubey Nikhil Agrawal Kavya Barnwal K. K. Guliani Pramod Mehta OOD AAML 104 47 0 16 May 2020
Adversarial Fooling Beyond "Flipping the Label" Konda Reddy Mopuri Vaisakh Shaj R. Venkatesh Babu AAML 64 12 0 27 Apr 2020
DeGAN : Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier Sravanti Addepalli Gaurav Kumar Nayak Anirban Chakraborty R. Venkatesh Babu 77 37 0 27 Dec 2019
Cross-Domain Transferability of Adversarial Perturbations Muzammal Naseer Salman H. Khan M. H. Khan Fahad Shahbaz Khan Fatih Porikli AAML 115 145 0 28 May 2019
Zero-Shot Knowledge Distillation in Deep Networks Gaurav Kumar Nayak Konda Reddy Mopuri Vaisakh Shaj R. Venkatesh Babu Anirban Chakraborty 75 245 0 20 May 2019
Universal Adversarial Training A. Mendrik Mahyar Najibi Zheng Xu John P. Dickerson L. Davis Tom Goldstein AAML OOD 102 190 0 27 Nov 2018
Generalizable Data-free Objective for Crafting Universal Adversarial Perturbations Konda Reddy Mopuri Aditya Ganeshan R. Venkatesh Babu AAML 134 206 0 24 Jan 2018