Attacking Deep Reinforcement Learning With Decoupled Adversarial Policy

Federated Learning (FL), as a privacy-preserving machine learning paradigm, has been thrusted into the limelight. As a result of the physical bandwidth constraint, only a small number of clients are selected for each round of FL training. However, existing client selection solutions (e.g., the vanilla random selection) typically ignore the heterogeneous data value of the clients. In this paper, we propose the contribution-based selection algorithm (Contribution-Based Exponentialweight algorithm for Exploration and Exploitation, CBE3), which dynamically updates the selection weights according to the impact of clients' data. As a novel component of CBE3, a scaling factor, which helps maintain a good balance between global model accuracy and convergence speed, is proposed to improve the algorithm's adaptability. Theoretically, we proved the regret bound of the proposed CBE3 algorithm, which demonstrates performance gaps between the CBE3 and the optimal choice. Empirically, extensive experiments conducted on Non-Independent Identically Distributed data demonstrate the superior performance of CBE3with up to 10% accuracy improvement compared with K-Center and Greedy and up to 100% faster convergence compared with the Random algorithm.

Section: Related Workmentioning

confidence: 99%

Contribution‐based Federated Learning client selection

Lin

Liu

et al. 2022

“…[5][6][7][8][9][10] These perturbations are imperceptible to human beings but can easily fool DNNs, which raises invisible threats to the vision-based automatic decision. [11][12][13][14][15] Consequently, the robustness of DNNs encounters great challenges in real-world applications. 16,17 For example, the existence of AEs can pose severe security threats for traffic sign recognition in autonomous driving.…”

Section: Introductionmentioning

confidence: 99%

Attention‐guided black‐box adversarial attacks with large‐scale multiobjective evolutionary optimization

Wang¹,

Yin²,

Jiang³

et al. 2022

Fooling deep neural networks (DNNs) with black-box optimization has become a popular adversarial attack fashion, as the prior structural knowledge of DNNs is always unknown. Nevertheless, recent black-box adversarial attacks may struggle to balance their attack ability and visual quality of the generated adversarial examples (AEs) in tackling high-resolution images. In this paper, we propose an attention-guided black-box adversarial attack based on the large-scale multiobjective evolutionary optimization, termed LMOA. By considering the spatial semantic information of images, we first take advantage of the attention map to determine the perturbed pixels. Instead of attacking the entire image, reducing the perturbed pixels with the attention mechanism can help to avoid the notorious curse of dimensionality and thereby improve the performance of attacking. Second, a large-scale multiobjective evolutionary algorithm traverse the reduced pixels in the salient region. Benefiting from its characteristics, the generated AEs can fool target DNNs while being invisible by human vision. Extensive experimental results have verified the effectiveness of the proposed LMOA on the ImageNet data set. More importantly, it is more competitive to generate highresolution AEs with the better visual quality than the existing black-box adversarial attacks.

“…15 They are also prone to different attacks, such as inversion attack, 16 mining attacks, 17,18 and so forth. [19][20][21][22] In general, machine learning can be divided into supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is widely used in solving classification and regression problems.…”

Section: Introductionmentioning

confidence: 99%

An improved integral distinguisher scheme based on neural networks

Zahednejad

Lyu

2022

At CRYPTO 2019, A. Gohr made a breakthrough in combining classical cryptanalysis and deep learning and applied his method to round reduced SPECK successfully. However, his suggested neural-based distinguisher scheme is only limited to differential cryptanalysis. In this paper, we have the following contributions:1. We combine integral cryptanalysis and deep learning to propose our neural-based integral distinguisher scheme for the first time. To illustrate the effectiveness of our distinguisher scheme, we apply it to block ciphers of different structures, such as substitution-permutation structure ciphers (PRESENT and RECTANGLE), Feistel structure cipher (LBLOCK), and addrotate-XOR cipher (SPECK) and compare the results with the state-of-the-art classical integral distinguishing method, namely, the bit-based division property. To our great surprise, our neural network-based integral distinguisher can extend the number of distinguished rounds for all block ciphers by two additional rounds (except RECTANGLE, where it is improved by one round) under the same data complexity.2. As an additional advantage of our scheme, we demonstrate that our Neural Distinguisher (ND) is not only helpful for block cipher designers but