SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution

Owe to the powerful generative priors, the pre-trained text-to-image (T2I) diffusion models have become increasingly popular in solving the real-world image super-resolution problem. However, as a consequence of the heavy quality degradation of input low-resolution (LR) images, the destruction of local structures can lead to ambiguous image semantics. As a result, the content of reproduced high-resolution image may have semantic errors, deteriorating the super-resolution performance. To address this issue, we present a semantics-aware approach to better preserve the semantic fidelity of generative real-world image super-resolution. First, we train a degradation-aware prompt extractor, which can generate accurate soft and hard semantic prompts even under strong degradation. The hard semantic prompts refer to the image tags, aiming to enhance the local perception ability of the T2I model, while the soft semantic prompts compensate for the hard ones to provide additional representation information. These semantic prompts can encourage the T2I model to generate detailed and semantically accurate results. Furthermore, during the inference process, we integrate the LR images into the initial sampling noise to mitigate the diffusion model's tendency to generate excessive random details. The experiments show that our method can reproduce more realistic image details and hold better the semantics

The Relationship between the Construction and Solution of the MILP Models and Applications

The automatic search method based on Mix-integer Linear Programming (MILP) is one of the most common tools to search the distinguishers of block ciphers. For differential analysis, the byte-oriented MILP model is usually used to count the number of differential active s-boxes and the bit-oriented MILP model is used to search the optimal differential characteristic. In this paper, we present the influences between the construction and solution of MILP models solved by Gurobi : 1). the number of variables; 2). the number of constraints; 3). the order of the constraints; 4). the order of variables in constraints. We carefully construct the MILP models according to these influences in order to find the desired results in a reasonable time. As applications, we search the differential characteristic of PRESENT,GIFT-64 and GIFT-128 in the single-key setting. We do a dual processing for the constraints of the s-box. It only takes 298 seconds to finish the search of the 8-round optimal differential characteristic based on the new MILP model. We also obtain the optimal differential characteristic of the 9/10/11-round PRESENT. With a special initial constraint, it only takes 4 seconds to obtain a 9-round differential characteristic with probability $2^{-42}$. We also get a 12/13-round differential characteristic with probability $2^{-58}/2^{-62}$. For GIFT-128, we improve the probability of differential characteristic of $9 \sim 21$ rounds and give the first attack on 26-round GIFT-128 based on a 20-round differential characteristic with probability $2^{-121.415}$

A Practical Chosen Message Power Analysis Approach Against Ciphers with the Key Whitening Layers

The key whitening is a technique intended to enhance the strength of a block cipher. Although some research work involves DPA attacks against the key whitening layer in the compact architecture, there are no literatures dedicated in the influence of the key whitening layers in the loop architecture from the standpoint of DPA. In this paper, we propose a practical chosen message power analysis approach against the loop architecture of ciphers with the key whitening layers, thus proving that the key whitening technique does not enhance the security of ciphers regard to DPA. Our approach follows a reduction strategy: we recover the whitening key in the general cipher with the key whitening layer and reduce other complicated key whitening layers to the general case. In order to further manifest the validity of the new approach, we carry extensive experiments on two ISO standardized ciphers CLEFIA and Camellia implemented in loop architecture on FPGA, and the keys are recovered as expected