Shangwei Guo

TL;DR: A systematic approach is proposed to discover the optimal policies for defending against different backdoor attacks by comprehensively evaluating 71 state-of-the-art data augmentation functions and envision this framework can be a good benchmark tool to advance future DNN backdoor studies.

TL;DR: Wang et al. as mentioned in this paper investigated the effectiveness of data augmentation techniques in mitigating backdoor attacks and enhancing DL models' robustness, and proposed a unified defense solution to fine-tune the infected model and eliminate the effects of the embedded backdoor; another augmentation policy to preprocess input samples and invalidate the triggers during inference.

TL;DR: The proposed VSI evaluates two aspects of the content similarity between plain and encrypted images: the edge similarity extracted via multi-threshold edge detection and the texture similarity measured by means of the co-occurrence matrix.

TL;DR: This paper proposes an affine transformation, which consists of permutation, diffusion and shift transformations, to protect the privacy of faces and proposes an optimization technique to increase the efficiency of encryption.

TL;DR: This paper proposes a new cloud database model by introducing computation service providers (CSPs), which can accommodate the conventional DBaaS model; the CSPs undertake most of the postprocessing and reconstruction burden for database query.