Related papers: A Survey of Adversarial Defenses in Vision-based Systems: Categorization, Methods and Challenges

A Survey of Adversarial Defenses in Vision-based Systems: Categorization, Methods and Challenges

URL: http://arxiv.org/abs/2503.00384v1
Date: Sat, 01 Mar 2025 07:17:18 GMT
Title: A Survey of Adversarial Defenses in Vision-based Systems: Categorization, Methods and Challenges
Authors: Nandish Chattopadhyay, Abdul Basit, Bassem Ouni, Muhammad Shafique,
Abstract summary: Adversarial attacks have emerged as a major challenge to the trustworthy deployment of machine learning models.<n>We present a comprehensive systematization of knowledge on adversarial defenses, focusing on two key computer vision tasks.<n>We map these defenses to the types of adversarial attacks and datasets where they are most effective.
Score: 4.716918459551686
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Adversarial attacks have emerged as a major challenge to the trustworthy deployment of machine learning models, particularly in computer vision applications. These attacks have a varied level of potency and can be implemented in both white box and black box approaches. Practical attacks include methods to manipulate the physical world and enforce adversarial behaviour by the corresponding target neural network models. Multiple different approaches to mitigate different kinds of such attacks are available in the literature, each with their own advantages and limitations. In this survey, we present a comprehensive systematization of knowledge on adversarial defenses, focusing on two key computer vision tasks: image classification and object detection. We review the state-of-the-art adversarial defense techniques and categorize them for easier comparison. In addition, we provide a schematic representation of these categories within the context of the overall machine learning pipeline, facilitating clearer understanding and benchmarking of defenses. Furthermore, we map these defenses to the types of adversarial attacks and datasets where they are most effective, offering practical insights for researchers and practitioners. This study is necessary for understanding the scope of how the available defenses are able to address the adversarial threats, and their shortcomings as well, which is necessary for driving the research in this area in the most appropriate direction, with the aim of building trustworthy AI systems for regular practical use-cases.

Related papers

Adversarial Attacks of Vision Tasks in the Past 10 Years: A Survey [21.4046846701173]
Adversarial attacks pose significant security threats during machine learning inference. Existing reviews often focus on attack classifications and lack comprehensive, in-depth analysis. This article addresses these gaps by offering a thorough summary of traditional and LVLM adversarial attacks.
arXiv Detail & Related papers (2024-10-31T07:22:51Z)
Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI [52.138044013005]
generative AI, particularly large language models (LLMs), become increasingly integrated into production applications. New attack surfaces and vulnerabilities emerge and put a focus on adversarial threats in natural language and multi-modal systems. Red-teaming has gained importance in proactively identifying weaknesses in these systems, while blue-teaming works to protect against such adversarial attacks. This work aims to bridge the gap between academic insights and practical security measures for the protection of generative AI systems.
arXiv Detail & Related papers (2024-09-23T10:18:10Z)
Attention-Based Real-Time Defenses for Physical Adversarial Attacks in Vision Applications [58.06882713631082]
Deep neural networks exhibit excellent performance in computer vision tasks, but their vulnerability to real-world adversarial attacks raises serious security concerns. This paper proposes an efficient attention-based defense mechanism that exploits adversarial channel-attention to quickly identify and track malicious objects in shallow network layers. It also introduces an efficient multi-frame defense framework, validating its efficacy through extensive experiments aimed at evaluating both defense performance and computational cost.
arXiv Detail & Related papers (2023-11-19T00:47:17Z)
Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook [2.1771693754641013]
We aim to provide a thorough understanding of the concept of physical adversarial attacks, analyzing their key characteristics and distinguishing features. Our article delves into various physical adversarial attack methods, categorized according to their target tasks in different applications. We assess the performance of these attack methods in terms of their effectiveness, stealthiness, and robustness.
arXiv Detail & Related papers (2023-08-11T15:02:19Z)
Physical Adversarial Attacks for Surveillance: A Survey [40.81031907691243]
This paper reviews recent attempts and findings in learning and designing physical adversarial attacks for surveillance applications. In particular, we propose a framework to analyze physical adversarial attacks and provide a comprehensive survey of physical adversarial attacks on four key surveillance tasks. The insights in this paper present an important step in building resilience within surveillance systems to physical adversarial attacks.
arXiv Detail & Related papers (2023-05-01T20:19:59Z)
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey [114.17568992164303]
Adrial attacks and defenses in machine learning and deep neural network have been gaining significant attention. This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques. New avenues of attack are also explored, including search-based, decision-based, drop-based, and physical-world attacks.
arXiv Detail & Related papers (2023-03-11T04:19:31Z)
Physical Adversarial Attack meets Computer Vision: A Decade Survey [55.38113802311365]
This paper presents a comprehensive overview of physical adversarial attacks. We take the first step to systematically evaluate the performance of physical adversarial attacks. Our proposed evaluation metric, hiPAA, comprises six perspectives.
arXiv Detail & Related papers (2022-09-30T01:59:53Z)
Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems [50.758281304737444]
We propose to explore adversarial examples and attack detection on reinforcement learning-based interactive recommendation systems. We first craft different types of adversarial examples by adding perturbations to the input and intervening on the casual factors. Then, we augment recommendation systems by detecting potential attacks with a deep learning-based classifier based on the crafted data.
arXiv Detail & Related papers (2021-12-02T04:12:24Z)
Adversarial Machine Learning for Cybersecurity and Computer Vision: Current Developments and Challenges [2.132096006921048]
Research in adversarial machine learning addresses a significant threat to the wide application of machine learning techniques. We first discuss three main categories of attacks against machine learning techniques -- poisoning attacks, evasion attacks, and privacy attacks. We notice adversarial samples in cybersecurity and computer vision are fundamentally different.
arXiv Detail & Related papers (2021-06-30T03:05:58Z)
Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain [58.30296637276011]
This paper summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques. It is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain.
arXiv Detail & Related papers (2020-07-05T18:22:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.