Retrieval-Augmented Convolutional Neural Networks Against Adversarial Examples
We propose a retrieval-augmented convolutional network (RaCNN) and propose to train it with local mixup, a novel variant of the recently proposed mixup algorithm. The proposed hybrid architecture combining a convolutional network and an off-the-shelf retrieval engine was designed to mitigate the adverse effect of off-manifold adversarial examples, while the proposed local mixup addresses on-manifold ones by explicitly encouraging the classifier to locally behave linearly on the data manifold. Our evaluation of the proposed approach against seven readilyavailable adversarial attacks on three datasets-CIFAR-10, SVHN and ImageNet-demonstrate the improved robustness compared to a vanilla convolutional network, and comparable performance with the state-of-the-art reactive defense approaches.
Code (0)
등록된 구현이 없습니다.
Tasks
RetrievalMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Retrieval-Augmented Convolutional Neural Networks for Improved Robustness against Adversarial Examples
We propose a retrieval-augmented convolutional network and propose to train it with local mixup, a novel variant of the recently proposed mixup algorithm. The proposed hybrid architecture combining a convolutional networ…
RetrievalThe Silent Saboteur: Imperceptible Adversarial Attacks against Black-Box Retrieval-Augmented Generation Systems
We explore adversarial attacks against retrieval-augmented generation (RAG) systems to identify their vulnerabilities. We focus on generating human-imperceptible adversarial examples and introduce a novel imperceptible r…
Answer GenerationQuestion AnsweringRAGRetrieval-augmented GenerationCgAT: Center-Guided Adversarial Training for Deep Hashing-Based Retrieval
Deep hashing has been extensively utilized in massive image retrieval because of its efficiency and effectiveness. However, deep hashing models are vulnerable to adversarial examples, making it essential to develop adver…
Adversarial AttackAdversarial DefenseDeep HashingImage Retrieval+3Evaluating and Safeguarding the Adversarial Robustness of Retrieval-Based In-Context Learning
With the emergence of large language models, such as LLaMA and OpenAI GPT-3, In-Context Learning (ICL) gained significant attention due to its effectiveness and efficiency. However, ICL is very sensitive to the choice, o…
Adversarial RobustnessIn-Context LearningRetrievalCountermeasures Against Adversarial Examples in Radio Signal Classification
Deep learning algorithms have been shown to be powerful in many communication network design problems, including that in automatic modulation classification. However, they are vulnerable to carefully crafted attacks call…
ClassificationDeep Learning