Ares: A System-Oriented Wargame Framework for Adversarial ML

• URL: http://arxiv.org/abs/2210.12952v1
• Date: Mon, 24 Oct 2022 04:55:18 GMT
• Title: Ares: A System-Oriented Wargame Framework for Adversarial ML
• Authors: Farhan Ahmed, Pratik Vaishnavi, Kevin Eykholt, Amir Rahmati
• Abstract summary: Ares is an evaluation framework for adversarial ML that allows researchers to explore attacks and defenses in a realistic wargame-like environment. Ares frames the conflict between the attacker and defender as two agents in a reinforcement learning environment with opposing objectives. This allows the introduction of system-level evaluation metrics such as time to failure and evaluation of complex strategies.
• Score: 3.197282271064602
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Since the discovery of adversarial attacks against machine learning models nearly a decade ago, research on adversarial machine learning has rapidly evolved into an eternal war between defenders, who seek to increase the robustness of ML models against adversarial attacks, and adversaries, who seek to develop better attacks capable of weakening or defeating these defenses. This domain, however, has found little buy-in from ML practitioners, who are neither overtly concerned about these attacks affecting their systems in the real world nor are willing to trade off the accuracy of their models in pursuit of robustness against these attacks. In this paper, we motivate the design and implementation of Ares, an evaluation framework for adversarial ML that allows researchers to explore attacks and defenses in a realistic wargame-like environment. Ares frames the conflict between the attacker and defender as two agents in a reinforcement learning environment with opposing objectives. This allows the introduction of system-level evaluation metrics such as time to failure and evaluation of complex strategies such as moving target defenses. We provide the results of our initial exploration involving a white-box attacker against an adversarially trained defender.

Related papers

• 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)
• A Novel Approach to Guard from Adversarial Attacks using Stable Diffusion [0.0]
  Our proposal suggests a different approach to the AI Guardian framework. Instead of including adversarial examples in the training process, we propose training the AI system without them. This aims to create a system that is inherently resilient to a wider range of attacks.
  arXiv  Detail & Related papers  (2024-05-03T04:08:15Z)
• Adversarial Markov Games: On Adaptive Decision-Based Attacks and Defenses [21.759075171536388]
  We show how attacks but also defenses can benefit by it and by learning from each other through interaction. We demonstrate that active defenses, which control how the system responds, are a necessary complement to model hardening when facing decision-based attacks. We lay out effective strategies in ensuring the robustness of ML-based systems deployed in the real-world.
  arXiv  Detail & Related papers  (2023-12-20T21:24:52Z)
• On the Difficulty of Defending Contrastive Learning against Backdoor Attacks [58.824074124014224]
  We show how contrastive backdoor attacks operate through distinctive mechanisms. Our findings highlight the need for defenses tailored to the specificities of contrastive backdoor attacks.
  arXiv  Detail & Related papers  (2023-12-14T15:54:52Z)
• BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning [85.2564206440109]
  This paper reveals the threats in this practical scenario that backdoor attacks can remain effective even after defenses. We introduce the emphtoolns attack, which is resistant to backdoor detection and model fine-tuning defenses.
  arXiv  Detail & Related papers  (2023-11-20T02:21:49Z)
• Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning [10.368343314144553]
  We provide a game-theoretic framework for ensemble adversarial attacks and defenses. We propose three new attack algorithms, specifically designed to target defenses with randomized transformations, multi-model voting schemes, and adversarial detector architectures.
  arXiv  Detail & Related papers  (2022-11-26T21:35:01Z)
• StratDef: Strategic Defense Against Adversarial Attacks in ML-based Malware Detection [0.0]
  StratDef is a strategic defense system based on a moving target defense approach. We show that StratDef performs better than other defenses even when facing the peak adversarial threat.
  arXiv  Detail & Related papers  (2022-02-15T16:51:53Z)
• Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDS [70.60975663021952]
  We study blackbox adversarial attacks on network classifiers. We argue that attacker-defender fixed points are themselves general-sum games with complex phase transitions. We show that a continual learning approach is required to study attacker-defender dynamics.
  arXiv  Detail & Related papers  (2021-11-23T23:42:16Z)
• Adversarial Attacks on ML Defense Models Competition [82.37504118766452]
  The TSAIL group at Tsinghua University and the Alibaba Security group organized this competition. The purpose of this competition is to motivate novel attack algorithms to evaluate adversarial robustness.
  arXiv  Detail & Related papers  (2021-10-15T12:12:41Z)
• Against All Odds: Winning the Defense Challenge in an Evasion Competition with Diversification [13.236009846517662]
  In this paper, we outline our learning-based system PEberus that got the first place in the defender challenge of the Microsoft Evasion Competition. Our system combines multiple, diverse defenses: we address the semantic gap, use various classification models, and apply a stateful defense.
  arXiv  Detail & Related papers  (2020-10-19T14:53:06Z)
• Deflecting Adversarial Attacks [94.85315681223702]
  We present a new approach towards ending this cycle where we "deflect" adversarial attacks by causing the attacker to produce an input that resembles the attack's target class. We first propose a stronger defense based on Capsule Networks that combines three detection mechanisms to achieve state-of-the-art detection performance.
  arXiv  Detail & Related papers  (2020-02-18T06:59:13Z)

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

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.