A Survey of Black-Box Adversarial Attacks on Computer Vision Models
By Siddhant Bhambri, Sumanyu Muku, Avinash Tulasi, Arun Balaji Buduru. 2019
91 citations recorded by the source corpus.
Machine learning has seen tremendous advances in the past few years, which has lead to deep learning models being deployed in varied applications of day-to-day life. Attacks on such models using perturbations, particularly in real-life scenarios, pose a severe challenge to their applicability, pushing research into the direction which aims to enhance the robustness of these models. After the introduction of these perturbations by Szegedy et al. [1], significant amount of research has focused on the reliability of such models, primarily in two aspects - white-box, where the adversary has access to the targeted model and related parameters; and the black-box, which resembles a real-life scenario with the adversary having almost no knowledge of the model to be attacked. To provide a comprehensive security cover, it is essential to identify, study, and build defenses against such attacks. Hence, in this paper, we propose to present a comprehensive comparative study of various black-box adversarial attacks and defense techniques.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Hardware and Architecture, Safety, Robustness & Interpretability, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yoshua Bengio
- Percy Liang
- William L. Hamilton
- Gauthier Gidel
- Bo Li
- Simon Lacoste-Julien
- Mohit Bansal
- Pascal Vincent
In Computer Vision and Pattern Recognition
- Simon Lacoste-Julien
- Bo Li
- Gauthier Gidel
- Luc Van Gool
- Xiaogang Wang
- Dacheng Tao
- Raquel Urtasun
- Andrew Zisserman
In Hardware and Architecture
In Safety, Robustness & Interpretability
- Avishek Joey Bose
- Bo Li
- William L. Hamilton
- Andre Cianflone
- Jinfeng Yi
- Simon Lacoste-Julien
- Bowen Zhou
- Pascal Vincent
In Signal Processing
- Pascal Vincent
- Gauthier Gidel
- Simon Lacoste-Julien
- Bo Li
- Bowen Zhou
- Avishek Joey Bose
- Jinfeng Yi
- Andre Cianflone
Citations in the corpus
- Adversarial Example Games (2020, background): “While there exists a vast literature of adversarial attacks, we focus on ones that are most related to our setting and direct the interested reader to comprehensive surveys for adversarial attacks and blackbox adversarial attacks [6, 14].”
- Trustworthy AI: From Principles to Practices (2021, background): “Various improvements have been studied to improve this defense [45, 229, 304].”