A Survey of Trojan Attacks and Defenses to Deep Neural Networks

• URL: http://arxiv.org/abs/2408.08920v1
• Date: Thu, 15 Aug 2024 04:20:32 GMT
• Title: A Survey of Trojan Attacks and Defenses to Deep Neural Networks
• Authors: Lingxin Jin, Xianyu Wen, Wei Jiang, Jinyu Zhan,
• Abstract summary: Deep Neural Networks (DNNs) have found extensive applications in safety-critical artificial intelligence systems. Recent research has revealed their susceptibility to Neural Network Trojans (NN Trojans) maliciously injected by adversaries.
• Score: 3.9444202574850755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep Neural Networks (DNNs) have found extensive applications in safety-critical artificial intelligence systems, such as autonomous driving and facial recognition systems. However, recent research has revealed their susceptibility to Neural Network Trojans (NN Trojans) maliciously injected by adversaries. This vulnerability arises due to the intricate architecture and opacity of DNNs, resulting in numerous redundant neurons embedded within the models. Adversaries exploit these vulnerabilities to conceal malicious Trojans within DNNs, thereby causing erroneous outputs and posing substantial threats to the efficacy of DNN-based applications. This article presents a comprehensive survey of Trojan attacks against DNNs and the countermeasure methods employed to mitigate them. Initially, we trace the evolution of the concept from traditional Trojans to NN Trojans, highlighting the feasibility and practicality of generating NN Trojans. Subsequently, we provide an overview of notable works encompassing various attack and defense strategies, facilitating a comparative analysis of their approaches. Through these discussions, we offer constructive insights aimed at refining these techniques. In recognition of the gravity and immediacy of this subject matter, we also assess the feasibility of deploying such attacks in real-world scenarios as opposed to controlled ideal datasets. The potential real-world implications underscore the urgency of addressing this issue effectively.

Related papers

• Flashy Backdoor: Real-world Environment Backdoor Attack on SNNs with DVS Cameras [11.658496836117907]
  We present the first evaluation of backdoor attacks in real-world environments on Spiking Neural Networks (SNNs) We present three novel backdoor attack methods on SNNs, i.e., Framed, Strobing, and Flashy Backdoor. Our results show that further research is needed to ensure the security of SNN-based systems against backdoor attacks and their safe application in real-world scenarios.
  arXiv  Detail & Related papers  (2024-11-05T11:44:54Z)
• Data Poisoning-based Backdoor Attack Framework against Supervised Learning Rules of Spiking Neural Networks [3.9444202574850755]
  Spiking Neural Networks (SNNs) are known for their low energy consumption and high robustness. This paper explores the robustness performance of SNNs trained by supervised learning rules under backdoor attacks.
  arXiv  Detail & Related papers  (2024-09-24T02:15:19Z)
• PerD: Perturbation Sensitivity-based Neural Trojan Detection Framework on NLP Applications [21.854581570954075]
  Trojan attacks embed the backdoor into the victim and is activated by the trigger in the input space. We propose a model-level Trojan detection framework by analyzing the deviation of the model output when we introduce a specially crafted perturbation to the input. We demonstrate the effectiveness of our proposed method on both a dataset of NLP models we create and a public dataset of Trojaned NLP models from TrojAI.
  arXiv  Detail & Related papers  (2022-08-08T22:50:03Z)
• Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks [98.21130211336964]
  Deep neural networks (DNNs) are known to be vulnerable to adversarial attacks. In this paper, we investigate the impact of network width and depth on the robustness of adversarially trained DNNs.
  arXiv  Detail & Related papers  (2021-10-07T23:13:33Z)
• Exploiting Vulnerabilities in Deep Neural Networks: Adversarial and Fault-Injection Attacks [14.958919450708157]
  We first discuss different vulnerabilities that can be exploited for generating security attacks for neural network-based systems. We then provide an overview of existing adversarial and fault-injection-based attacks on DNNs.
  arXiv  Detail & Related papers  (2021-05-05T08:11:03Z)
• Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases [87.69818690239627]
  We study the problem of the Trojan network (TrojanNet) detection in the data-scarce regime. We propose a data-limited TrojanNet detector (TND), when only a few data samples are available for TrojanNet detection. In addition, we propose a data-free TND, which can detect a TrojanNet without accessing any data samples.
  arXiv  Detail & Related papers  (2020-07-31T02:00:38Z)
• Cassandra: Detecting Trojaned Networks from Adversarial Perturbations [92.43879594465422]
  In many cases, pre-trained models are sourced from vendors who may have disrupted the training pipeline to insert Trojan behaviors into the models. We propose a method to verify if a pre-trained model is Trojaned or benign. Our method captures fingerprints of neural networks in the form of adversarial perturbations learned from the network gradients.
  arXiv  Detail & Related papers  (2020-07-28T19:00:40Z)
• An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks [59.42357806777537]
  trojan attack aims to attack deployed deep neural networks (DNNs) relying on hidden trigger patterns inserted by hackers. We propose a training-free attack approach which is different from previous work, in which trojaned behaviors are injected by retraining model on a poisoned dataset. The proposed TrojanNet has several nice properties including (1) it activates by tiny trigger patterns and keeps silent for other signals, (2) it is model-agnostic and could be injected into most DNNs, dramatically expanding its attack scenarios, and (3) the training-free mechanism saves massive training efforts compared to conventional trojan attack methods.
  arXiv  Detail & Related papers  (2020-06-15T04:58:28Z)
• Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies [73.39668293190019]
  Adversary attacks can be easily fooled by small perturbation on the input. Graph Neural Networks (GNNs) have been demonstrated to inherit this vulnerability. In this survey, we categorize existing attacks and defenses, and review the corresponding state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-03-02T04:32:38Z)
• Defending against Backdoor Attack on Deep Neural Networks [98.45955746226106]
  We study the so-called textitbackdoor attack, which injects a backdoor trigger to a small portion of training data. Experiments show that our method could effectively decrease the attack success rate, and also hold a high classification accuracy for clean images.
  arXiv  Detail & Related papers  (2020-02-26T02:03:00Z)

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.