Quan Chen

Jingyu Liu, Minquan Wang, Ye Ma et al.

Videos showcasing specific products are increasingly important for E-commerce. Key moments naturally exist as the first appearance of a specific product, presentation of its distinctive features, the presence of a buying link, etc. Adding proper sound effects (SFX) to these key moments, or video decoration with SFX (VDSFX), is crucial for enhancing the user engaging experience. Previous studies about adding SFX to videos perform video to SFX matching at a holistic level, lacking the ability of adding SFX to a specific moment. Meanwhile, previous studies on video highlight detection or video moment retrieval consider only moment localization, leaving moment to SFX matching untouched. By contrast, we propose in this paper D&M, a unified method that accomplishes key moment detection and moment to SFX matching simultaneously. Moreover, for the new VDSFX task we build a large-scale dataset SFX-Moment from an E-commerce platform. For a fair comparison, we build competitive baselines by extending a number of current video moment detection methods to the new task. Extensive experiments on SFX-Moment show the superior performance of the proposed method over the baselines.

Chen Quan, Baocheng Geng, Yunghsiang S. Han et al.

This paper employs an audit bit based mechanism to mitigate the effect of Byzantine attacks. In this framework, the optimal attacking strategy for intelligent attackers is investigated for the traditional audit bit based scheme (TAS) to evaluate the robustness of the system. We show that it is possible for an intelligent attacker to degrade the performance of TAS to the system without audit bits. To enhance the robustness of the system in the presence of intelligent attackers, we propose an enhanced audit bit based scheme (EAS). The optimal fusion rule for the proposed scheme is derived and the detection performance of the system is evaluated via the probability of error for the system. Simulation results show that the proposed EAS improves the robustness and the detection performance of the system. Moreover, based on EAS, another new scheme called the reduced audit bit based scheme (RAS) is proposed which further improves system performance. We derive the new optimal fusion rule and the simulation results show that RAS outperforms EAS and TAS in terms of both robustness and detection performance of the system. Then, we extend the proposed RAS for a wide-area cluster based distributed wireless sensor networks (CWSNs). Simulation results show that the proposed RAS significantly reduces the communication overhead between the sensors and the FC, which prolongs the lifetime of the network.

Quan Chen, Peter Snyder, Ben Livshits et al.

Content blocking is an important part of a performant, user-serving, privacy respecting web. Most content blockers build trust labels over URLs. While useful, this approach has well understood shortcomings. Attackers may avoid detection by changing URLs or domains, bundling unwanted code with benign code, or inlining code in pages. The common flaw in existing approaches is that they evaluate code based on its delivery mechanism, not its behavior. In this work we address this problem with a system for generating signatures of the privacy-and-security relevant behavior of executed JavaScript. Our system considers script behavior during each turn on the JavaScript event loop. Focusing on event loop turns allows us to build signatures that are robust against code obfuscation, code bundling, URL modification, and other common evasions, as well as handle unique aspects of web applications. This work makes the following contributions to improving content blocking: First, implement a novel system to build per-event-loop-turn signatures of JavaScript code by instrumenting the Blink and V8 runtimes. Second, we apply these signatures to measure filter list evasion, by using EasyList and EasyPrivacy as ground truth and finding other code that behaves identically. We build ~2m signatures of privacy-and-security behaviors from 11,212 unique scripts blocked by filter lists, and find 3,589 more unique scripts including the same harmful code, affecting 12.48% of websites measured. Third, we taxonomize common filter list evasion techniques. Finally, we present defenses; filter list additions where possible, and a proposed, signature based system in other cases. We share the implementation of our signature-generation system, the dataset from applying our system to the Alexa 100K, and 586 AdBlock Plus compatible filter list rules to block instances of currently blocked code being moved to new URLs.

Quan Chen, Tiezheng Ge, Yanyu Xu et al.

Human matting, high quality extraction of humans from natural images, is crucial for a wide variety of applications. Since the matting problem is severely under-constrained, most previous methods require user interactions to take user designated trimaps or scribbles as constraints. This user-in-the-loop nature makes them difficult to be applied to large scale data or time-sensitive scenarios. In this paper, instead of using explicit user input constraints, we employ implicit semantic constraints learned from data and propose an automatic human matting algorithm (SHM). SHM is the first algorithm that learns to jointly fit both semantic information and high quality details with deep networks. In practice, simultaneously learning both coarse semantics and fine details is challenging. We propose a novel fusion strategy which naturally gives a probabilistic estimation of the alpha matte. We also construct a very large dataset with high quality annotations consisting of 35,513 unique foregrounds to facilitate the learning and evaluation of human matting. Extensive experiments on this dataset and plenty of real images show that SHM achieves comparable results with state-of-the-art interactive matting methods.