DEFEAT: Deep Hidden Feature Backdoor Attacks by Imperceptible Perturbation and Latent Representation Constraints
Backdoor attack is a type of serious security threat to deep learning models.An adversary can provide users with a model trained on poisoned data to manipulate prediction behavior in test stage using a backdoor. The backdoored models behave normally on clean images, yet can be activated and output incorrect prediction if the input is stamped with a specific trigger pattern.Most existing backdoor attacks focus on manually defining imperceptible triggers in input space without considering the abnormality of triggers' latent representations in the poisoned model.These attacks are susceptible to backdoor detection algorithms and even visual inspection.In this paper, We propose a novel and stealthy backdoor attack - DEFEAT. It poisons the clean data using adaptive imperceptible perturbation and restricts latent representation during training process to strengthen our attack's stealthiness and resistance to defense algorithms.We conduct extensive experiments on multiple image classifiers using real-world datasets to demonstrate that our attack can 1) hold against the state-of-the-art defenses, 2) deceive the victim model with high attack success without jeopardizing model utility, and 3) provide practical stealthiness on image data.
Code (0)
등록된 구현이 없습니다.
Tasks
Backdoor AttackSimilar Papers 제목 키워드 기반
Color Backdoor: A Robust Poisoning Attack in Color Space
Backdoor attacks against neural networks have been intensively investigated, where the adversary compromises the integrity of the victim model, causing it to make wrong predictions for inference samples containing a …
Backdoor AttackSSIMA semantic backdoor attack against Graph Convolutional Networks
Graph convolutional networks (GCNs) have been very effective in addressing the issue of various graph-structured related tasks. However, recent research has shown that GCNs are vulnerable to a new type of threat called a…
Backdoor AttackClassificationGraph ClassificationNode ClassificationBackdoor Attack with Imperceptible Input and Latent Modification
Recent studies have shown that deep neural networks (DNN) are vulnerable to various adversarial attacks. In particular, an adversary can inject a stealthy backdoor into a model such that the compromised model will behave…
Backdoor AttackInvisible Backdoor Attack Through Singular Value Decomposition
With the widespread application of deep learning across various domains, concerns about its security have grown significantly. Among these, backdoor attacks pose a serious security threat to deep neural networks (DNNs). …
Backdoor AttackDoes Few-shot Learning Suffer from Backdoor Attacks?
The field of few-shot learning (FSL) has shown promising results in scenarios where training data is limited, but its vulnerability to backdoor attacks remains largely unexplored. We first explore this topic by first eva…
Backdoor AttackFew-Shot Learning