Relevant bibliographies by topics / Backdoor attacks

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Backdoor attacks'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Backdoor attacks"

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Duan, Qiuyu, Zhongyun Hua, Qing Liao, Yushu Zhang, and Leo Yu Zhang. "Conditional Backdoor Attack via JPEG Compression." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 18 (2024): 19823–31. http://dx.doi.org/10.1609/aaai.v38i18.29957.

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Deep neural network (DNN) models have been proven vulnerable to backdoor attacks. One trend of backdoor attacks is developing more invisible and dynamic triggers to make attacks stealthier. However, these invisible and dynamic triggers can be inadvertently mitigated by some widely used passive denoising operations, such as image compression, making the efforts under this trend questionable. Another trend is to exploit the full potential of backdoor attacks by proposing new triggering paradigms, such as hibernated or opportunistic backdoors. In line with these trends, our work investigates the
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Zhu, Biru, Ganqu Cui, Yangyi Chen, et al. "Removing Backdoors in Pre-trained Models by Regularized Continual Pre-training." Transactions of the Association for Computational Linguistics 11 (2023): 1608–23. http://dx.doi.org/10.1162/tacl_a_00622.

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Abstract Recent research has revealed that pre-trained models (PTMs) are vulnerable to backdoor attacks before the fine-tuning stage. The attackers can implant transferable task-agnostic backdoors in PTMs, and control model outputs on any downstream task, which poses severe security threats to all downstream applications. Existing backdoor-removal defenses focus on task-specific classification models and they are not suitable for defending PTMs against task-agnostic backdoor attacks. To this end, we propose the first task-agnostic backdoor removal method for PTMs. Based on the selective activa
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Wang, Qingya, Yi Wu, Haojun Xuan, and Huishu Wu. "FLARE: A Backdoor Attack to Federated Learning with Refined Evasion." Mathematics 12, no. 23 (2024): 3751. http://dx.doi.org/10.3390/math12233751.

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Federated Learning (FL) is vulnerable to backdoor attacks in which attackers inject malicious behaviors into the global model. To counter these attacks, existing works mainly introduce sophisticated defenses by analyzing model parameters and utilizing robust aggregation strategies. However, we find that FL systems can still be attacked by exploiting their inherent complexity. In this paper, we propose a novel three-stage backdoor attack strategy named FLARE: A Backdoor Attack to Federated Learning with Refined Evasion, which is designed to operate under the radar of conventional defense strate
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Yuan, Guotao, Hong Huang, and Xin Li. "Self-supervised learning backdoor defense mixed with self-attention mechanism." Journal of Computing and Electronic Information Management 12, no. 2 (2024): 81–88. http://dx.doi.org/10.54097/7hx9afkw.

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Recent studies have shown that Deep Neural Networks (DNNs) are vulnerable to backdoor attacks, where attackers embed hidden backdoors into the DNN models by poisoning a small number of training samples. The attacked models perform normally on benign samples, but when the backdoor is activated, their prediction results will be maliciously altered. To address the issues of suboptimal backdoor defense effectiveness and limited generality, a hybrid self-attention mechanism-based self-supervised learning method for backdoor defense is proposed. This method defends against backdoor attacks by levera
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Zhang, Fan, Jianpeng Li, Wei Huang, and Xi Chen. "BMAIU: Backdoor Mitigation in Self-Supervised Learning Through Active Implantation and Unlearning." Electronics 14, no. 8 (2025): 1587. https://doi.org/10.3390/electronics14081587.

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Self-supervised learning (SSL) is vulnerable to backdoor attacks, while the downstream classifiers based on SSL models inevitably inherit these backdoors, even when they are trained on clean samples. Despite the proposal of several methods of backdoor defense against backdoor attacks, few methods remain that can be used to effectively defend against various backdoor attacks while maintaining the high performance of the model. In this paper, based on the discovery that unlearning any trigger enhances the overall backdoor robustness of the model, a novel, efficient, and straightforward approach
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Yin, Jia-Li, Weijian Wang, Lyhwa, Wei Lin, and Ximeng Liu. "Adversarial-Inspired Backdoor Defense via Bridging Backdoor and Adversarial Attacks." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 9 (2025): 9508–16. https://doi.org/10.1609/aaai.v39i9.33030.

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Backdoor attacks and adversarial attacks are two major security threats to deep neural networks (DNNs), with the former one is a training-time data poisoning attack that aims to implant backdoor triggers into models by injecting trigger patterns into training samples, and the latter one is a testing-time attack trying to generate adversarial examples (AEs) from benign images to mislead a well-trained model. While previous works generally treat these two attacks separately, the inherent connection between these two attacks is rarely explored. In this paper, we focus on bridging backdoor and adv
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Wang, Tong, Yuan Yao, Feng Xu, Miao Xu, Shengwei An, and Ting Wang. "Inspecting Prediction Confidence for Detecting Black-Box Backdoor Attacks." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 1 (2024): 274–82. http://dx.doi.org/10.1609/aaai.v38i1.27780.

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Backdoor attacks have been shown to be a serious security threat against deep learning models, and various defenses have been proposed to detect whether a model is backdoored or not. However, as indicated by a recent black-box attack, existing defenses can be easily bypassed by implanting the backdoor in the frequency domain. To this end, we propose a new defense DTInspector against black-box backdoor attacks, based on a new observation related to the prediction confidence of learning models. That is, to achieve a high attack success rate with a small amount of poisoned data, backdoor attacks
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Jiang, Peihai, Xixiang Lyu, Yige Li, and Jing Ma. "Backdoor Token Unlearning: Exposing and Defending Backdoors in Pretrained Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 23 (2025): 24285–93. https://doi.org/10.1609/aaai.v39i23.34605.

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Supervised fine-tuning has become the predominant method for adapting large pretrained models to downstream tasks. However, recent studies have revealed that these models are vulnerable to backdoor attacks, where even a small number of malicious samples can successfully embed backdoor triggers into the model. While most existing defense methods focus on post-training backdoor defense, efficiently defending against backdoor attacks during training phase remains largely unexplored. To address this gap, we propose a novel defense method called Backdoor Token Unlearning (BTU), which proactively de
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Saha, Aniruddha, Akshayvarun Subramanya, and Hamed Pirsiavash. "Hidden Trigger Backdoor Attacks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11957–65. http://dx.doi.org/10.1609/aaai.v34i07.6871.

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With the success of deep learning algorithms in various domains, studying adversarial attacks to secure deep models in real world applications has become an important research topic. Backdoor attacks are a form of adversarial attacks on deep networks where the attacker provides poisoned data to the victim to train the model with, and then activates the attack by showing a specific small trigger pattern at the test time. Most state-of-the-art backdoor attacks either provide mislabeled poisoning data that is possible to identify by visual inspection, reveal the trigger in the poisoned data, or u
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Zhang, Xianda, Baolin Zheng, Jianbao Hu, Chengyang Li, and Xiaoying Bai. "From Toxic to Trustworthy: Using Self-Distillation and Semi-supervised Methods to Refine Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (2024): 16873–80. http://dx.doi.org/10.1609/aaai.v38i15.29629.

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Despite the tremendous success of deep neural networks (DNNs) across various fields, their susceptibility to potential backdoor attacks seriously threatens their application security, particularly in safety-critical or security-sensitive ones. Given this growing threat, there is a pressing need for research into purging backdoors from DNNs. However, prior efforts on erasing backdoor triggers not only failed to withstand increasingly powerful attacks but also resulted in reduced model performance. In this paper, we propose From Toxic to Trustworthy (FTT), an innovative approach to eliminate bac
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Dissertations / Theses on the topic "Backdoor attacks"

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Turner, Alexander M. S. M. Massachusetts Institute of Technology. "Exploring the landscape of backdoor attacks on deep neural network models." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123127.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 71-75).<br>Deep neural networks have recently been demonstrated to be vulnerable to backdoor attacks. Specifically, by introducing a small set of training inputs, an adversary is able to plant a backdoor in the trained model t
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Espinoza, Castellon Fabiola. "Contributions to effective and secure federated learning with client data heterogeneity." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG007.

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Cette thèse se penche sur deux défis de l'apprentissage fédéré: l'hétérogénéité des données et la sécurité des modèles. Dans la première partie, nous nous attaquons à l'hétérogénéité des données, une problématique inhérente aux applications d'apprentissage fédéré dans un cadre réaliste. Les clients peuvent avoir des distributions de données différentes à cause de leurs opinions, localisations ou habitudes. Nous nous concentrons sur deux types distincts d'hétérogénéité dans les tâches de classification. Premièrement, quand les participants ont des distributions de données différentes mais simil
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(9034049), Miguel Villarreal-Vasquez. "Anomaly Detection and Security Deep Learning Methods Under Adversarial Situation." Thesis, 2020.

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<p>Advances in Artificial Intelligence (AI), or more precisely on Neural Networks (NNs), and fast processing technologies (e.g. Graphic Processing Units or GPUs) in recent years have positioned NNs as one of the main machine learning algorithms used to solved a diversity of problems in both academia and the industry. While they have been proved to be effective in solving many tasks, the lack of security guarantees and understanding of their internal processing disrupts their wide adoption in general and cybersecurity-related applications. In this dissertation, we present the findings of a comp
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Books on the topic "Backdoor attacks"

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Li, Shaofeng, Haojin Zhu, Wen Wu, and Xuemin Shen. Backdoor Attacks against Learning-Based Algorithms. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57389-7.

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Backdoor Attacks Against Learning-Based Algorithms. Springer, 2024.

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Michael A, Newton. Part IV The ICC and its Applicable Law, 29 Charging War Crimes: Policy and Prognosis from a Military Perspective. Oxford University Press, 2015. http://dx.doi.org/10.1093/law/9780198705161.003.0029.

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The Rome Statute was designed to largely align criminal norms with actual state practice based on the realities of warfare. Article 8 embodied notable new refinements (e.g. in relation to disproportionate attack under Article 8(2)(b)(iv)), but did so against a backdrop of pragmatic military practice. This chapter dissects the structure of war crimes under Rome Statute to demonstrate this deliberate intention of Article 8 and then describes the correlative considerations related to charging practices for the maturing institution, including command responsibility. When properly understood and ap
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Yaari, Nurit. The Trojan War and the Israeli–Palestinian Conflict. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198746676.003.0006.

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This chapter looks at Israeli productions of classical tragedies between 1970 and 1985, against the backdrop of four wars: the Six Day War (1967), the War of Attrition (1967–70), the Yom Kippur War (1973), and the First Lebanon War (1982–5). The tragedies in question recount two fateful and bloody wars of antiquity: the second Persian offensive against Greece (480–479 BCE) which serves as the background to Aeschylusʼ The Persians (472 BCE), and the Trojan War—the prehistoric battle immortalized by Homer in The Iliad and The Odyssey, and in the tragedies of Aeschylus, Sophocles, and Euripides.
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Shenouda, Anthony St. At War in Prayer. Rowman & Littlefield, 2020. https://doi.org/10.5040/9781978718074.

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The practice of continuous prayer has been known in the Christian church as early as the second century CE, well before the beginning of Christian monasticism. One of the ways early Christians practiced continuous prayer was through the repetition of short bible verses throughout the day. While this mode of prayer did not have any specific name until the twentieth century, its practice has always been characterized by the imagery of warfare and, more specifically, the use of arrows. It was probably this that gave rise to its name, the Arrow Prayer, on account of its brevity and its use to atta
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Bartley, Abel A. Keeping the Faith. Greenwood Publishing Group, Inc., 2000. http://dx.doi.org/10.5040/9798400675553.

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An examination of the political and economic power of a large African American community in a segregated southern city; this study attacks the myth that blacks were passive victims of the southern Jim Crow system and reveals instead that in Jacksonville, Florida, blacks used political and economic pressure to improve their situation and force politicians to make moderate adjustments in the Jim Crow system. Bartley tells the compelling story of how African Americans first gained, then lost, then regained political representation in Jacksonville. Between the end of the Civil War and the consolid
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Chorev, Nitsan. Give and Take. Princeton University Press, 2019. http://dx.doi.org/10.23943/princeton/9780691197845.001.0001.

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This book looks at local drug manufacturing in Kenya, Tanzania, and Uganda, from the early 1980s to the present, to understand the impact of foreign aid on industrial development. While foreign aid has been attacked by critics as wasteful, counterproductive, or exploitative, this book makes a clear case for the effectiveness of what it terms “developmental foreign aid.” Against the backdrop of Africa’s pursuit of economic self-sufficiency, the battle against AIDS and malaria, and bitter negotiations over affordable drugs, the book offers an important corrective to popular views on foreign aid
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Book chapters on the topic "Backdoor attacks"

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Rong, Dazhong, Guoyao Yu, Shuheng Shen, et al. "Clean-Image Backdoor Attacks." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72359-9_14.

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Pham, Long H., and Jun Sun. "Verifying Neural Networks Against Backdoor Attacks." In Computer Aided Verification. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13185-1_9.

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AbstractNeural networks have achieved state-of-the-art performance in solving many problems, including many applications in safety/security-critical systems. Researchers also discovered multiple security issues associated with neural networks. One of them is backdoor attacks, i.e., a neural network may be embedded with a backdoor such that a target output is almost always generated in the presence of a trigger. Existing defense approaches mostly focus on detecting whether a neural network is ‘backdoored’ based on heuristics, e.g., activation patterns. To the best of our knowledge, the only lin
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Li, Shaofeng, Haojin Zhu, Wen Wu, and Xuemin Shen. "Literature Review of Backdoor Attacks." In Wireless Networks. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57389-7_2.

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Chan, Shih-Han, Yinpeng Dong, Jun Zhu, Xiaolu Zhang, and Jun Zhou. "BadDet: Backdoor Attacks on Object Detection." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25056-9_26.

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Li, Shaofeng, Haojin Zhu, Wen Wu, and Xuemin Shen. "Backdoor Attacks and Defense in FL." In Wireless Networks. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57389-7_5.

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Chen, Jinyin, Ximin Zhang, and Haibin Zheng. "Backdoor Attack on Dynamic Link Prediction." In Attacks, Defenses and Testing for Deep Learning. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0425-5_7.

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Fu, Hao, Alireza Sarmadi, Prashanth Krishnamurthy, Siddharth Garg, and Farshad Khorrami. "Mitigating Backdoor Attacks on Deep Neural Networks." In Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40677-5_16.

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Xin, Jinwen, Xixiang Lyu, and Jing Ma. "Natural Backdoor Attacks on Speech Recognition Models." In Machine Learning for Cyber Security. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-20096-0_45.

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Narisada, Shintaro, Yuki Matsumoto, Seira Hidano, et al. "Countermeasures Against Backdoor Attacks Towards Malware Detectors." In Cryptology and Network Security. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92548-2_16.

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Wang, Ruofei, Qing Guo, Haoliang Li, and Renjie Wan. "Event Trojan: Asynchronous Event-Based Backdoor Attacks." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72667-5_18.

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Conference papers on the topic "Backdoor attacks"

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Wu, Chen, Sencun Zhu, Prasenjit Mitra, and Wei Wang. "Unlearning Backdoor Attacks in Federated Learning." In 2024 IEEE Conference on Communications and Network Security (CNS). IEEE, 2024. http://dx.doi.org/10.1109/cns62487.2024.10735680.

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Yin, Wen, Jian Lou, Pan Zhou, et al. "Physical Backdoor: Towards Temperature-Based Backdoor Attacks in the Physical World." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.01210.

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Al Kader Hammoud, Hasan Abed, Shuming Liu, Mohammed Alkhrashi, Fahad AlBalawi, and Bernard Ghanem. "Look, Listen, and Attack: Backdoor Attacks Against Video Action Recognition." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00348.

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Naseri, Mohammad, Yufei Han, and Emiliano De Cristofaro. "BadVFL: Backdoor Attacks in Vertical Federated Learning." In 2024 IEEE Symposium on Security and Privacy (SP). IEEE, 2024. http://dx.doi.org/10.1109/sp54263.2024.00008.

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Qiu, Yuran, Huy H. Nguyen, Qingyao Liao, Chun-Shien Lu, and Isao Echizen. "Analysis of Backdoor Attacks on Deepfake Detection." In 2024 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2024. http://dx.doi.org/10.1109/ijcb62174.2024.10744504.

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Zhao, Tianya, Ningning Wang, Yanzhao Wu, Wenbin Zhang, and Xuyu Wang. "Backdoor Attacks Against Low-Earth Orbit Satellite Fingerprinting." In IEEE INFOCOM 2024 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2024. http://dx.doi.org/10.1109/infocomwkshps61880.2024.10620841.

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Kao, Ching-Chia, Cheng-Yi Lee, Chun-Shien Lu, Chia-Mu Yu, and Chu-Song Chen. "On the Higher Moment Disparity of Backdoor Attacks." In 2024 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2024. http://dx.doi.org/10.1109/icme57554.2024.10687873.

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Zhang, Kaiyuan, Siyuan Cheng, Guangyu Shen, et al. "Exploring the Orthogonality and Linearity of Backdoor Attacks." In 2024 IEEE Symposium on Security and Privacy (SP). IEEE, 2024. http://dx.doi.org/10.1109/sp54263.2024.00225.

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Dunnett, Kealan, Reza Arablouei, Dimity Miller, Volkan Dedeoglu, and Raja Jurdak. "Unlearning Backdoor Attacks Through Gradient-Based Model Pruning." In 2024 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W). IEEE, 2024. http://dx.doi.org/10.1109/dsn-w60302.2024.00021.

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Shin, Jeongjin. "Mask-Based Invisible Backdoor Attacks on Object Detection." In 2024 IEEE International Conference on Image Processing (ICIP). IEEE, 2024. http://dx.doi.org/10.1109/icip51287.2024.10647450.

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Reports on the topic "Backdoor attacks"

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Yang, Shuhan. Backdoor attack in autonomous vehicles. Iowa State University, 2023. http://dx.doi.org/10.31274/cc-20240624-276.

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Lewis, Dustin, ed. Database of States’ Statements (August 2011–October 2016) concerning Use of Force in relation to Syria. Harvard Law School Program on International Law and Armed Conflict, 2017. http://dx.doi.org/10.54813/ekmb4241.

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Many see armed conflict in Syria as a flashpoint for international law. The situation raises numerous unsettling questions, not least concerning normative foundations of the contemporary collective-security and human-security systems, including the following: Amid recurring reports of attacks directed against civilian populations and hospitals with seeming impunity, what loss of legitimacy might law suffer? May—and should—states forcibly intervene to prevent (more) chemical-weapons attacks? If the government of Syria is considered unwilling or unable to obviate terrorist threats from spilling
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Bourekba, Moussa. Climate Change and Violent Extremism in North Africa. The Barcelona Centre for International Affairs, 2021. http://dx.doi.org/10.55317/casc014.

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As climate change intensifies in many parts of the world, more and more policymakers are concerned with its effects on human security and violence. From Lake Chad to the Philippines, including Afghanistan and Syria, some violent extremist (VE) groups such as Boko Haram and the Islamic State exploit crises and conflicts resulting from environmental stress to recruit more followers, expand their influence and even gain territorial control. In such cases, climate change may be described as a “risk multiplier” that exacerbates a number of conflict drivers. Against this backdrop, this case study lo
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