Improving Visual Quality and Transferability of Adversarial Attacks on Face Recognition Simultaneously with Adversarial Restoration

Adversarial face examples possess two critical properties: Visual Quality and Transferability. However, existing approaches rarely address these properties simultaneously, leading to subpar results. To address this issue, we propose a novel adversarial attack technique known as Adversarial Restoration (AdvRestore), which enhances both visual quality and transferability of adversarial face examples by leveraging a face restoration prior. In our approach, we initially train a Restoration Latent Diffusion Model (RLDM) designed for face restoration. Subsequently, we employ the inference process of RLDM to generate adversarial face examples. The adversarial perturbations are applied to the intermediate features of RLDM. Additionally, by treating RLDM face restoration as a sibling task, the transferability of the generated adversarial face examples is further improved. Our experimental results validate the effectiveness of the proposed attack method.Comment: \copyright 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Improving the Transferability of Adversarial Attacks on Face Recognition with Beneficial Perturbation Feature Augmentation

Face recognition (FR) models can be easily fooled by adversarial examples, which are crafted by adding imperceptible perturbations on benign face images. To improve the transferability of adversarial face examples, we propose a novel attack method called Beneficial Perturbation Feature Augmentation Attack (BPFA), which reduces the overfitting of adversarial examples to surrogate FR models by constantly generating new models that have the similar effect of hard samples to craft the adversarial examples. Specifically, in the backpropagation, BPFA records the gradients on pre-selected features and uses the gradient on the input image to craft the adversarial example. In the next forward propagation, BPFA leverages the recorded gradients to add perturbations (i.e., beneficial perturbations) that can be pitted against the adversarial example on their corresponding features. The optimization process of the adversarial example and the optimization process of the beneficial perturbations added on the features correspond to a minimax two-player game. Extensive experiments demonstrate that BPFA can significantly boost the transferability of adversarial attacks on FR

Synthesis and curing properties of multifunctional castor oil-based epoxy resin

A series of multifunctional castor oil-based epoxy resins, named methoxy castor oil polyglycidyl ether (MOCOGE), allyloxy castor oil polyglycidyl ether (AOCOGE), and castor oil-based non-glycidyl ether (CONGE), was designed through a ring-opening etherification reaction to broaden the application of E−51, a common type of bisphenol A epoxy resin. Research on its curing properties showed that adding castor-oil-based epoxy resins greatly enhanced the flexibility, toughness, and mechanical strength of E−51. In particular, when the blending amount was 10%, the tensile strength increased by 31.4%, 29.1%, and 34.4% compared to that of pure E−51, reaching 84.13, 82.66, and 86.07 MPa, respectively. The elongation at break increased by 29.2%, 29.8%, and 21.9%, respectively, and the impact strength increased by 59.5%, 39.5%, and 53.1%, respectively. Our results indicate that these castor oil-based epoxy resins improved E−51 properties, expanding the application of castor oil-based epoxy resins