Compressive Sensing Based Adaptive Defence Against Adversarial Images
Herein, security of deep neural network against adversarial attack is considered. Existing compressive sensing based defence schemes assume that adversarial perturbations are usually on high frequency components, whereas recently it has been shown that low frequency perturbations are more effective. This paper proposes a novel Compressive sensing based Adaptive Defence (CAD) algorithm which combats distortion in frequency domain instead of time domain. Unlike existing literature, the proposed CAD algorithm does not use information about the type of attack such as l0, l2, l-infinity etc. CAD algorithm uses exponential weight algorithm for exploration and exploitation to identify the type of attack, compressive sampling matching pursuit (CoSaMP) to recover the coefficients in spectral domain, and modified basis pursuit using a novel constraint for l0, l-infinity norm attack. Tight performance bounds for various recovery schemes meant for various attack types are also provided. Experimental results against five state-of-the-art white box attacks on MNIST and CIFAR-10 show that the proposed CAD algorithm achieves excellent classification accuracy and generates good quality reconstructed image with much lower computation
Code (0)
등록된 구현이 없습니다.
Tasks
Adversarial AttackCompressive SensingSimilar Papers 제목 키워드 기반
Carefully Blending Adversarial Training, Purification, and Aggregation Improves Adversarial Robustness
In this work, we propose a novel adversarial defence mechanism for image classification - CARSO - blending the paradigms of adversarial training and adversarial purification in a synergistic robustness-enhancing way. The…
Adversarial DefenseAdversarial PurificationAdversarial Robustnessimage-classification+2Security and Privacy for Artificial Intelligence: Opportunities and Challenges
The increased adoption of Artificial Intelligence (AI) presents an opportunity to solve many socio-economic and environmental challenges; however, this cannot happen without securing AI-enabled technologies. In recent ye…
Federated LearningDefending against Adversarial Attack towards Deep Neural Networks via Collaborative Multi-task Training
Deep neural networks (DNNs) are known to be vulnerable to adversarial examples which contain human-imperceptible perturbations. A series of defending methods, either proactive defence or reactive defence, have been propo…
Adversarial AttackDefending a Music Recommender Against Hubness-Based Adversarial Attacks
Adversarial attacks can drastically degrade performance of recommenders and other machine learning systems, resulting in an increased demand for defence mechanisms. We present a new line of defence against attacks which …
Counter-Samples: A Stateless Strategy to Neutralize Black Box Adversarial Attacks
Our paper presents a novel defence against black box attacks, where attackers use the victim model as an oracle to craft their adversarial examples. Unlike traditional preprocessing defences that rely on sanitizing input…