Diffusion-Based Purification (DBP) has emerged as an effective defense mechanism against adversarial attacks. The success of DBP is often attributed to the forward diffusion process, which reduces the distribution gap between clean and adversarial images by adding Gaussian noise. Although this explanation is theoretically grounded, the precise contribution of this process to robustness remains unclear. In this paper, through a systematic investigation, we propose that the intrinsic stochasticity in the DBP procedure is the primary factor driving robustness. To explore this hypothesis, we introduce a novel Deterministic White-Box (DW-box) evaluation protocol to assess robustness in the absence of stochasticity, and analyze attack trajectories and loss landscapes. Our results suggest that DBP models primarily leverage stochasticity to evade effective attack directions, and that their ability to purify adversarial perturbations can be weak. To further enhance the robustness of DBP models, we propose Adversarial Denoising Diffusion Training (ADDT), which incorporates classifier-guided adversarial perturbations into diffusion training, thereby strengthening the models' ability to purify adversarial perturbations. Additionally, we propose Rank-Based Gaussian Mapping (RBGM) to improve the compatibility of perturbations with diffusion models. Experimental results validate the effectiveness of ADDT. In conclusion, our study suggests that future research on DBP can benefit from the perspective of decoupling stochasticity-based and purification-based robustness.

In recent years, extensive research has been conducted on the design of electricity auctions. However, most existing solutions focus mainly on privacy preservation, efficiency, and bidding strategies, while overlooking the critical issue of key parameters distribution—particularly in day-ahead electricity auctions. At the start of an auction, it is essential to distribute key parameters to all participants in a timely, synchronized, and efficient manner. To address the challenges of fairness, collusion resistance, and efficiency in the pre-distribution of key parameters, we propose an efficient and collusion-resistant key parameters pre-distribution system for day-ahead electricity auctions. As a core component, we design a novel cryptographic primitive, termed identity-based timed-release proxy re-encryption. Based on the truncated q-ABDHE hardness assumption, we instantiate two concrete schemes tailored to different security requirements. The proposed schemes eliminate the need for public key authenticity verification, support efficient privilege revocation, and provide resistance against proxy-bidder collusion attacks. Additionally, the schemes feature user-scale-independent key and ciphertext sizes, leading to significantly improved system efficiency compared to existing approaches.

Generative adversarial networks (GANs) are subject to catastrophic forgetting when learning stream of data. Inspired by the knowledge of neuroscience, this article develops a memory formation system (MFS) to establish memory for GANs. MFS is composed of three modules, including the identifier, weights upgrade (WU), and weights reactivate (WR). These modules simulate the function of encoding, consolidating, and retrieving in memory formation of human. Identifier creates indexes to label continuous tasks and these indexes are used as a cue when the corresponding tasks are recalled. WU and WR work as synaptic consolidation and system consolidation, respectively. In WU, a novel method, weight saliency measure (WSM) is proposed to measure the saliency of weight. Valuable weights are protected and invaluable weights are released for update when GAN is trained for the new task. In WR, traditional data-replay methods are improved by selecting${k}$representatives in each class and their feature vectors are restored. When pseudo data of this class are regenerated, only the samples with distance less than a threshold can be used in future training. Experimental results based on the testing of continual image generation and continual 3-D reconstruction show that MFS can establish a memory system to handle the catastrophic forgetting problem effectively.