Physical Unclonable Functions (PUFs) are widely used in hardware security due to their inherent unclonability and randomness. However, the temporal instability of strong PUFs remains a barrier to their adoption in latest PUF-based cryptographic protocols, as it incurs significant overhead from error correction. In this paper, we propose PS-PUF, a novel architecture that leverages weak PUFs and cryptographically secure pseudorandom functions (PRFs) to construct a pseudo-strong PUF with stable and reproducible outputs. Our design includes a PRF for secure mapping, and a buffer to optimize performance in batch-access scenarios. We formally analyze the threat surface of PS-PUF and provide cryptographic security proofs showing resistance against modeling attacks. Implemented on the Genesys 2 FPGA, PS-PUF achieves at least 2.72× in batch scenarios with negligible hardware overhead and a maximum performance reduction of 10.7%, enabled by reusing the PRF module in integrated environments.

A deraining network can be interpreted as a conditional generator that aims at removing rain streaks from image. Most existing image deraining methods ignore model errors caused by uncertainty that reduces embedding quality. Unlike existing image deraining methods that embed low-quality features into the model directly, we replace low-quality features by latent high-quality features. The spirit of closed-loop feedback in the automatic control field is borrowed to obtain latent high-quality features. A new method for error detection and feature compensation is proposed to address model errors. Extensive experiments on bench-mark datasets as well as specific real datasets demonstrate that the proposed method outperforms recent state-of-the-art methods. Code is available at: https://github.com/LI-Hao-SJTU/DerainRLNet