Yi Tang

Yi Tang, Takafumi Iwaguchi, Hiroshi Kawasaki

In this paper, we present an approach to image enhancement with diffusion model in underwater scenes. Our method adapts conditional denoising diffusion probabilistic models to generate the corresponding enhanced images by using the underwater images and the Gaussian noise as the inputs. Additionally, in order to improve the efficiency of the reverse process in the diffusion model, we adopt two different ways. We firstly propose a lightweight transformer-based denoising network, which can effectively promote the time of network forward per iteration. On the other hand, we introduce a skip sampling strategy to reduce the number of iterations. Besides, based on the skip sampling strategy, we propose two different non-uniform sampling methods for the sequence of the time step, namely piecewise sampling and searching with the evolutionary algorithm. Both of them are effective and can further improve performance by using the same steps against the previous uniform sampling. In the end, we conduct a relative evaluation of the widely used underwater enhancement datasets between the recent state-of-the-art methods and the proposed approach. The experimental results prove that our approach can achieve both competitive performance and high efficiency. Our code is available at \href{mailto:https://github.com/piggy2009/DM_underwater}{\color{blue}{https://github.com/piggy2009/DM\_underwater}}.

Pengwei Zhan, Liming Wang, Yi Tang

Cryptographic protocols have been widely used to protect the user's privacy and avoid exposing private information. QUIC (Quick UDP Internet Connections), including the version originally designed by Google (GQUIC) and the version standardized by IETF (IQUIC), as alternatives to the traditional HTTP, demonstrate their unique transmission characteristics: based on UDP for encrypted resource transmitting, accelerating web page rendering. However, existing encrypted transmission schemes based on TCP are vulnerable to website fingerprinting (WFP) attacks, allowing adversaries to infer the users' visited websites by eavesdropping on the transmission channel. Whether GQUIC and IQUIC can effectively resist such attacks is worth investigating. In this paper, we study the vulnerabilities of GQUIC, IQUIC, and HTTPS to WFP attacks from the perspective of traffic analysis. Extensive experiments show that, in the early traffic scenario, GQUIC is the most vulnerable to WFP attacks among GQUIC, IQUIC, and HTTPS, while IQUIC is more vulnerable than HTTPS, but the vulnerability of the three protocols is similar in the normal full traffic scenario. Features transferring analysis shows that most features are transferable between protocols when on normal full traffic scenario. However, combining with the qualitative analysis of latent feature representation, we find that the transferring is inefficient when on early traffic, as GQUIC, IQUIC, and HTTPS show the significantly different magnitude of variation in the traffic distribution on early traffic. By upgrading the one-time WFP attacks to multiple WFP Top-a attacks, we find that the attack accuracy on GQUIC and IQUIC reach 95.4% and 95.5%, respectively, with only 40 packets and just using simple features, whereas reach only 60.7% when on HTTPS. We also demonstrate that the vulnerability of IQUIC is only slightly dependent on the network environment.

Joan Bruna, Oded Regev, Min Jae Song et al.

We introduce a continuous analogue of the Learning with Errors (LWE) problem, which we name CLWE. We give a polynomial-time quantum reduction from worst-case lattice problems to CLWE, showing that CLWE enjoys similar hardness guarantees to those of LWE. Alternatively, our result can also be seen as opening new avenues of (quantum) attacks on lattice problems. Our work resolves an open problem regarding the computational complexity of learning mixtures of Gaussians without separability assumptions (Diakonikolas 2016, Moitra 2018). As an additional motivation, (a slight variant of) CLWE was considered in the context of robust machine learning (Diakonikolas et al.~FOCS 2017), where hardness in the statistical query (SQ) model was shown; our work addresses the open question regarding its computational hardness (Bubeck et al.~ICML 2019).