REFINE: Inversion-Free Backdoor Defense via Model Reprogramming
Zhejiang University（浙江大学）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新区（滨江）区块链与数据安全研究院）；Nanyang Technological University（南洋理工大学）；IBM Research（IBM研究院）
Backdoor attacks on deep neural networks (DNNs) have emerged as a significant security threat, allowing adversaries to implant hidden malicious behaviors during the model training phase. Pre-processing-based defense, which is one of the most important defense paradigms, typically focuses on input transformations or backdoor trigger inversion (BTI) to deactivate or eliminate embedded backdoor triggers during the inference process. However, these methods suffer from inherent limitations: transformation-based defenses often fail to balance model utility and defense performance, while BTI-based defenses struggle to accurately reconstruct trigger patterns without prior knowledge. In this paper, we propose REFINE, an inversion-free backdoor defense method based on model reprogramming. REFINE consists of two key components: \textbf{(1)} an input transformation module that disrupts both benign and backdoor patterns, generating new benign features; and \textbf{(2)} an output remapping module that redefines the model's output domain to guide the input transformations effectively. By further integrating supervised contrastive loss, REFINE enhances the defense capabilities while maintaining model utility. Extensive experiments on various benchmark datasets demonstrate the effectiveness of our REFINE and its resistance to potential adaptive attacks.
Proactive Adversarial Defense: Harnessing Prompt Tuning in Vision-Language Models to Detect Unseen Backdoored Images
University of West Florida（西佛罗里达大学）；Florida Institute For Human and Machine Cognition (IHMC)（佛罗里达人机认知研究所）；Center for Cybersecurity, University of West Florida（西佛罗里达大学网络安全中心）
Tongji University（同济大学）；Pennsylvania State University（宾夕法尼亚州立大学）
Federated Learning (FL) is a popular distributed machine learning paradigm that enables jointly training a global model without sharing clients' data. However, its repetitive server-client communication gives room for backdoor attacks with aim to mislead the global model into a targeted misprediction when a specific trigger pattern is presented. In response to such backdoor threats on federated learning, various defense measures have been proposed. In this paper, we study whether the current defense mechanisms truly neutralize the backdoor threats from federated learning in a practical setting…
Deep neural networks for image classification are well-known to be vulnerable to adversarial attacks. One such attack that has garnered recent attention is the adversarial backdoor attack, which has demonstrated the capability to perform targeted misclassification of specific examples. In particular, backdoor attacks attempt to force a model to learn spurious relations between backdoor trigger patterns and false labels. In response to this threat, numerous defensive measures have been proposed; however, defenses against backdoor attacks focus on backdoor pattern detection, which may be unrelia…
Carnegie Mellon University（卡内基梅隆大学）；University of Washington（华盛顿大学）
Backdoor attacks are dangerous and difficult to prevent in federated learning (FL), where training data is sourced from untrusted clients over long periods of time. These difficulties arise because: (a) defenders in FL do not have access to raw training data, and (b) a new phenomenon we identify called backdoor leakage causes models trained continuously to eventually suffer from backdoors due to cumulative errors in defense mechanisms. We propose shadow learning, a framework for defending against backdoor attacks in the FL setting under long-range training. Shadow learning trains two models in…
Fuzhou University（福州大学）；East China Normal University（华东师范大学）
The opacity of neural networks leads their vulnerability to backdoor attacks, where hidden attention of infected neurons is triggered to override normal predictions to the attacker-chosen ones. In this paper, we propose a novel backdoor defense method to mark and purify the infected neurons in the backdoored neural networks. Specifically, we first define a new metric, called benign salience. By combining the first-order gradient to retain the connections between neurons, benign salience can identify the infected neurons with higher accuracy than the commonly used metric in backdoor defense. Th…
Princeton University（普林斯顿大学）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
Recent studies revealed that deep learning is susceptible to backdoor poisoning attacks. An adversary can embed a hidden backdoor into a model to manipulate its predictions by only modifying a few training data, without controlling the training process. Currently, a tangible signature has been widely observed across a diverse set of backdoor poisoning attacks -- models trained on a poisoned dataset tend to learn separable latent representations for poison and clean samples. This latent separation is so pervasive that a family of backdoor defenses directly take it as a default assumption (dubbe…
University of Science and Technology of China（中国科学技术大学）；Hong Kong University of Science and Technology（香港科技大学）
Deep neural networks (DNNs) are recently shown to be vulnerable to backdoor attacks, where attackers embed hidden backdoors in the DNN model by injecting a few poisoned examples into the training dataset. While extensive efforts have been made to detect and remove backdoors from backdoored DNNs, it is still not clear whether a backdoor-free clean model can be directly obtained from poisoned datasets. In this paper, we first construct a causal graph to model the generation process of poisoned data and find that the backdoor attack acts as the confounder, which brings spurious associations betwe…
Learning Normal Diffusion Dynamics for Backdoor Defense in Text-to-Image Models
Geely（吉利）；Hunan University（湖南大学）；East China Normal University（华东师范大学）；University of Adelaide（阿德莱德大学）；The Hong Kong University of Science and Technology（香港科技大学）；China University of Petroleum (East China)（中国石油大学（华东））
Cert-SSBD: Certified Backdoor Defense with Sample-Specific Smoothing Noises
School of Control and Computer Engineering, North China Electric Power University（控制与计算机工程学院，华北电力大学）；Research Institute, China Unicom（中国unicom研究院）；National Engineering Research Center of Next Generation Internet Broadband Service Application（下一代互联网宽带服务应用国家工程研究中心）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）
Backdoor Vectors: a Task Arithmetic View on Backdoor Attacks and Defenses
Warsaw University of Technology（华沙技术大学）；NASK National Research Institute（国家研究机构NASK）；IDEAS NCBR；IDEAS Research Institute（IDEAS研究所）；Computer Vision Center, Universitat Autonoma de Barcelona（巴塞罗那自治大学计算机视觉中心）
Backdoor attack intends to embed hidden backdoor into deep neural networks (DNNs), such that the attacked model performs well on benign samples, whereas its prediction will be maliciously changed if the hidden backdoor is activated by the attacker defined trigger. This threat could happen when the training process is not fully controlled, such as training on third-party data-sets or adopting third-party models. There has been a lot of research and different methods to defend such type of backdoor attacks, one being robustness-aware perturbation-based defense method. This method mainly exploits…
Backdoor defenses have been studied to alleviate the threat of deep neural networks (DNNs) being backdoor attacked and thus maliciously altered. Since DNNs usually adopt some external training data from an untrusted third party, a robust backdoor defense strategy during the training stage is of importance. We argue that the core of training-time defense is to select poisoned samples and to handle them properly. In this work, we summarize the training-time defenses from a unified framework as splitting the poisoned dataset into two data pools. Under our framework, we propose an adaptively split…
University of Washington（华盛顿大学）；Western Washington University（西华盛顿大学）
Machine learning (ML) models that use deep neural networks are vulnerable to backdoor attacks. Such attacks involve the insertion of a (hidden) trigger by an adversary. As a consequence, any input that contains the trigger will cause the neural network to misclassify the input to a (single) target class, while classifying other inputs without a trigger correctly. ML models that contain a backdoor are called Trojan models. Backdoors can have severe consequences in safety-critical cyber and cyber physical systems when only the outputs of the model are available. Defense mechanisms have been deve…
University of Maryland（马里兰大学）；Massachussetts Institute of Technology（麻省理工学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California, Berkeley（加利福尼亚大学伯克利分校）
As machine learning systems grow in scale, so do their training data requirements, forcing practitioners to automate and outsource the curation of training data in order to achieve state-of-the-art performance. The absence of trustworthy human supervision over the data collection process exposes organizations to security vulnerabilities; training data can be manipulated to control and degrade the downstream behaviors of learned models. The goal of this work is to systematically categorize and discuss a wide range of dataset vulnerabilities and exploits, approaches for defending against these t…
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Research Center of Artificial Intelligence, Peng Cheng Laboratory（鹏城实验室人工智能研究中心）
Recent studies have demonstrated that deep neural networks (DNNs) are vulnerable to backdoor attacks during the training process. Specifically, the adversaries intend to embed hidden backdoors in DNNs so that malicious model predictions can be activated through pre-defined trigger patterns. In this paper, we explore the backdoor mechanism from the angle of the model structure. We select the skip connection for discussions, inspired by the understanding that it helps the learning of model `shortcuts' where backdoor triggers are usually easier to be learned. Specifically, we demonstrate that the…
Reconstructive Neuron Pruning for Backdoor Defense
Xidian University（西安电子科技大学）；Fudan University（复旦大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of Copenhagen（哥本哈根大学）；Sony AI（索尼人工智能）；University of Illinois at Urbana–Champaign（伊利诺伊大学厄巴纳-香槟分校）
Defending the Edge: Representative-Attention Defense against Backdoor Attacks in Federated Learning
Dept. of Computer Science Faculty of Engineering University of Manchester Manchester, United Kingdom；Dept. of Computer Science Faculty of Engineering University of Manchester \& MBZUAI Abu Dhabi, United Arab Emirates；Dept. of Computer Science Faculty of Engineering University of Manchester \& KU Leuven Manchester, United Kingdom
MARS: A Malignity-Aware Backdoor Defense in Federated Learning
Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）；School of Computing, Australian National University（澳大利亚国立大学计算机学院）；Ant Group（蚂蚁集团）；School of Cyber Science and Engineering, Huazhong University of Science and Technology（华中科技大学网络安全工程学院）；School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
Federated Learning (FL) is a distributed learning paradigm that enables different parties to train a model together for high quality and strong privacy protection. In this scenario, individual participants may get compromised and perform backdoor attacks by poisoning the data (or gradients). Existing work on robust aggregation and certified FL robustness does not study how hardening benign clients can affect the global model (and the malicious clients). In this work, we theoretically analyze the connection among cross-entropy loss, attack success rate, and clean accuracy in this setting. Moreo…
Pennsylvania State University（宾夕法尼亚州立大学）；Shanghai Jiao Tong University（上海交通大学）
Backdoor attacks (BA) are an emerging threat to deep neural network classifiers. A classifier being attacked will predict to the attacker's target class when a test sample from a source class is embedded with the backdoor pattern (BP). Recently, the first BA against point cloud (PC) classifiers was proposed, creating new threats to many important applications including autonomous driving. Such PC BAs are not detectable by existing BA defenses due to their special BP embedding mechanism. In this paper, we propose a reverse-engineering defense that infers whether a PC classifier is backdoor atta…
Recent exploration of the multi-agent backdoor attack demonstrated the backfiring effect, a natural defense against backdoor attacks where backdoored inputs are randomly classified. This yields a side-effect of low accuracy w.r.t. clean labels, which motivates this paper's work on the construction of multi-agent backdoor defenses that maximize accuracy w.r.t. clean labels and minimize that of poison labels. Founded upon agent dynamics and low-loss subspace construction, we contribute three defenses that yield improved multi-agent backdoor robustness.
Backdoor (Trojan) attacks are emerging threats against deep neural networks (DNN). A DNN being attacked will predict to an attacker-desired target class whenever a test sample from any source class is embedded with a backdoor pattern; while correctly classifying clean (attack-free) test samples. Existing backdoor defenses have shown success in detecting whether a DNN is attacked and in reverse-engineering the backdoor pattern in a "post-training" regime: the defender has access to the DNN to be inspected and a small, clean dataset collected independently, but has no access to the (possibly poi…
Pennsylvania State University（宾夕法尼亚州立大学）；Anomalee Inc.（Anomalee 公司）；Imperial College（帝国理工学院）
We demonstrate a backdoor attack on a deep neural network used for regression. The backdoor attack is localized based on training-set data poisoning wherein the mislabeled samples are surrounded by correctly labeled ones. We demonstrate how such localization is necessary for attack success. We also study the performance of a backdoor defense using gradient-based discovery of local error maximizers. Local error maximizers which are associated with significant (interpolation) error, and are proximal to many training samples, are suspicious. This method is also used to accurately train for deep r…
Towards Backdoor Stealthiness in Model Parameter Space
Radboud University（拉德博德大学）；Delft University of Technology（代尔夫特理工大学）；Radboud University, Nijmegen, The Netherlands（拉德博德大学）；University of Zagreb Faculty of Electrical Engineering and Computing（Zagreb 工程与计算学院）
Zhejiang University（浙江大学）；Tsinghua University（清华大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
Recent studies have revealed that deep neural networks (DNNs) are vulnerable to backdoor attacks, where attackers embed hidden backdoors in the DNN model by poisoning a few training samples. The attacked model behaves normally on benign samples, whereas its prediction will be maliciously changed when the backdoor is activated. We reveal that poisoned samples tend to cluster together in the feature space of the attacked DNN model, which is mostly due to the end-to-end supervised training paradigm. Inspired by this observation, we propose a novel backdoor defense via decoupling the original end-…
SoK: The Last Line of Defense: On Backdoor Defense Evaluation
University of Bergen（卑尔根大学）；Radboud University（拉德博德大学）；Delft University of Technology（代尔夫特理工大学）；University of Pavia（帕维亚大学）；Ikerlan Research Center Radboud University（Ikerlan研究中心拉德博德大学）
CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）；University of Birmingham（伯明翰大学）；University of Lausanne（洛桑大学）
Backdoor attacks represent one of the major threats to machine learning models. Various efforts have been made to mitigate backdoors. However, existing defenses have become increasingly complex and often require high computational resources or may also jeopardize models' utility. In this work, we show that fine-tuning, one of the most common and easy-to-adopt machine learning training operations, can effectively remove backdoors from machine learning models while maintaining high model utility. Extensive experiments over three machine learning paradigms show that fine-tuning and our newly prop…
Backdoor attacks embed hidden malicious behaviors into deep learning models, which only activate and cause misclassifications on model inputs containing a specific trigger. Existing works on backdoor attacks and defenses, however, mostly focus on digital attacks that use digitally generated patterns as triggers. A critical question remains unanswered: can backdoor attacks succeed using physical objects as triggers, thus making them a credible threat against deep learning systems in the real world? We conduct a detailed empirical study to explore this question for facial recognition, a critical…
Nanjing University（南京大学）；Purdue University（普渡大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Pennsylvania State University（宾夕法尼亚州立大学）
Backdoor attacks have been shown to be a serious threat against deep learning systems such as biometric authentication and autonomous driving. An effective backdoor attack could enforce the model misbehave under certain predefined conditions, i.e., triggers, but behave normally otherwise. However, the triggers of existing attacks are directly injected in the pixel space, which tend to be detectable by existing defenses and visually identifiable at both training and inference stages. In this paper, we propose a new backdoor attack FTROJAN through trojaning the frequency domain. The key intuitio…
The goal of federated learning (FL) is to train one global model by aggregating model parameters updated independently on edge devices without accessing users' private data. However, FL is susceptible to backdoor attacks where a small fraction of malicious agents inject a targeted misclassification behavior in the global model by uploading polluted model updates to the server. In this work, we propose DifFense, an automated defense framework to protect an FL system from backdoor attacks by leveraging differential testing and two-step MAD outlier detection, without requiring any previous knowle…
FedBAP: Backdoor Defense via Benign Adversarial Perturbation in Federated Learning
School of Cyber Science and Engineering, Wuhan University（武汉大学计算机科学与工程学院）；Department of Computer and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机与人工智能学院）；School of Software Engineering, Huazhong University of Science and Technology（华中科技大学软件工程学院）
