A Diffusion Model Based Quality Enhancement Method for HEVC Compressed Video

Video post-processing methods can improve the quality of compressed videos at the decoder side. Most of the existing methods need to train corresponding models for compressed videos with different quantization parameters to improve the quality of compressed videos. However, in most cases, the quantization parameters of the decoded video are unknown. This makes existing methods have their limitations in improving video quality. To tackle this problem, this work proposes a diffusion model based post-processing method for compressed videos. The proposed method first estimates the feature vectors of the compressed video and then uses the estimated feature vectors as the prior information for the quality enhancement model to adaptively enhance the quality of compressed video with different quantization parameters. Experimental results show that the quality enhancement results of our proposed method on mixed datasets are superior to existing methods.Comment: 10 pages, conferenc

IEEE Access Special Section Editorial : Trust Management in Pervasive Social Networking (TruPSN)

Non peer reviewe

Improved Factoring Attacks on Multi-Prime RSA with Small Prime Difference

In this paper, we study the security of multi-prime RSA with small prime difference and propose two improved factoring attacks. The modulus involved in this variant is the product of r distinct prime factors of the same bit-size. Zhang and Takagi (ACISP 2013) showed a Fermat-like factoring attack on multi-prime RSA. In order to improve the previous result, we gather more information about the prime factors to derive r simultaneous modular equations. The first attack is to combine all the equations and solve one multivariate equation by generic lattice approaches. Since the equation form is similar to multi-prime Phi-hiding problem, we propose the second attack by applying the optimal linearization technique. We also show that our attacks can achieve better bounds in the experiments

Towards Strengthening Deep Learning-based Side Channel Attacks with Mixup

In recent years, various deep learning techniques have been exploited in side channel attacks, with the anticipation of obtaining more appreciable attack results. Most of them concentrate on improving network architectures or putting forward novel algorithms, assuming that there are adequate profiling traces available to train an appropriate neural network. However, in practical scenarios, profiling traces are probably insufficient, which makes the network learn deficiently and compromises attack performance. In this paper, we investigate a kind of data augmentation technique, called mixup, and first propose to exploit it in deep-learning based side channel attacks, for the purpose of expanding the profiling set and facilitating the chances of mounting a successful attack. We perform Correlation Power Analysis for generated traces and original traces, and discover that there exists consistency between them regarding leakage information. Our experiments show that mixup is truly capable of enhancing attack performance especially for insufficient profiling traces. Specifically, when the size of the training set is decreased to 30% of the original set, mixup can significantly reduce acquired attacking traces. We test three mixup parameter values and conclude that generally all of them can bring about improvements. Besides, we compare three leakage models and unexpectedly find that least significant bit model, which is less frequently used in previous works, actually surpasses prevalent identity model and hamming weight model in terms of attack results

Enhancing the Performance of Practical Profiling Side-Channel Attacks Using Conditional Generative Adversarial Networks

Recently, many profiling side-channel attacks based on Machine Learning and Deep Learning have been proposed. Most of them focus on reducing the number of traces required for successful attacks by optimizing the modeling algorithms. In previous work, relatively sufficient traces need to be used for training a model. However, in the practical profiling phase, it is difficult or impossible to collect sufficient traces due to the constraint of various resources. In this case, the performance of profiling attacks is inefficient even if proper modeling algorithms are used. In this paper, the main problem we consider is how to conduct more efficient profiling attacks when sufficient profiling traces cannot be obtained. To deal with this problem, we first introduce the Conditional Generative Adversarial Network (CGAN) in the context of side-channel attacks. We show that CGAN can generate new traces to enlarge the size of the profiling set, which improves the performance of profiling attacks. For both unprotected and protected cryptographic algorithms, we find that CGAN can effectively learn the leakage of traces collected in their implementations. We also apply it to different modeling algorithms. In our experiments, the model constructed with the augmented profiling set can reduce the required attack traces by more than half, which means the generated traces can provide useful information as the real traces

IGF-1R Inhibition Induces MEK Phosphorylation to Promote Survival in Colon Carcinomas

The insulin-like growth factor 1 receptor (IGF-1R) governs several signaling pathways for cell proliferation, survival, and anti-apoptosis. Thus, targeting IGF-1R appears as a reasonable rationale for tumor treatment. However, clinical studies showed that inhibition of IGF-1R has very limited efficacy due to the development of resistance to IGF-1R blockade in tumor cells. Here, we discovered that prolonged treatment of colon cancer cells with IGF-1R inhibitors (BMS-754807 and GSK1838705A) stimulates p70 KDa ribosomal protein S6 kinase 1 (p70S6K1) activation, a well-known kinase signaling for cell survival. We also found that p70S6K1 activation by IGF-1R inhibition is independent of K-Ras and PIK3CA mutations that frequently occur in colon cancer. Besides the increased p70S6K1 phosphorylation, the phosphorylation of mitogen-activated protein kinase kinase 1 and 2 (MEK1/2) was elevated in the cells treated with BMS-754807. Interestingly, the increases in MEK1/2 and p70S6K1 phosphorylation were also observed when cells were subjected to the treatment of AKT inhibitor or genetic knockdown of AKT2 but not AKT1, suggesting that AKT2 inhibition stimulates MEK1/2 phosphorylation to activate p70S6K1. Conversely, inhibition of MEK1/2 by MEK1/2 inhibitor (U0126) or knockdown of MEK1 and MEK2 by corresponding mek1 and mek2 siRNA enhanced AKT phosphorylation, indicating mutual inhibition between AKT and MEK. Furthermore, the combination of BMS-754807 and U0126 efficiently decreased the cell viability and increased cleaved caspase 3 and apoptosis in vitro and in vivo. Our data suggest that the treatment of colon tumor cells with IGF-1R inhibitors stimulates p70S6K1 activity via MEK1/2 to promote survival, providing a new strategy for colorectal cancer therapeutics