Song Li

Han Wang, Jun Tang, Xiaodong Liu et al.

Recent years have witnessed a trend of applying context frames to boost the performance of object detection as video object detection. Existing methods usually aggregate features at one stroke to enhance the feature. These methods, however, usually lack spatial information from neighboring frames and suffer from insufficient feature aggregation. To address the issues, we perform a progressive way to introduce both temporal information and spatial information for an integrated enhancement. The temporal information is introduced by the temporal feature aggregation model (TFAM), by conducting an attention mechanism between the context frames and the target frame (i.e., the frame to be detected). Meanwhile, we employ a Spatial Transition Awareness Model (STAM) to convey the location transition information between each context frame and target frame. Built upon a transformer-based detector DETR, our PTSEFormer also follows an end-to-end fashion to avoid heavy post-processing procedures while achieving 88.1% mAP on the ImageNet VID dataset. Codes are available at https://github.com/Hon-Wong/PTSEFormer.

Hengsheng Zhang, Xueyi Zou, Jiaming Guo et al.

Online processing of compressed videos to increase their resolutions attracts increasing and broad attention. Video Super-Resolution (VSR) using recurrent neural network architecture is a promising solution due to its efficient modeling of long-range temporal dependencies. However, state-of-the-art recurrent VSR models still require significant computation to obtain a good performance, mainly because of the complicated motion estimation for frame/feature alignment and the redundant processing of consecutive video frames. In this paper, considering the characteristics of compressed videos, we propose a Codec Information Assisted Framework (CIAF) to boost and accelerate recurrent VSR models for compressed videos. Firstly, the framework reuses the coded video information of Motion Vectors to model the temporal relationships between adjacent frames. Experiments demonstrate that the models with Motion Vector based alignment can significantly boost the performance with negligible additional computation, even comparable to those using more complex optical flow based alignment. Secondly, by further making use of the coded video information of Residuals, the framework can be informed to skip the computation on redundant pixels. Experiments demonstrate that the proposed framework can save up to 70% of the computation without performance drop on the REDS4 test videos encoded by H.264 when CRF is 23.

Jingchuan Hu, Shuai Guo, Kai Zhou et al.

As an important application form of immersive multimedia services, free-viewpoint video(FVV) enables users with great immersive experience by strong interaction. However, the computational complexity of virtual view synthesis algorithms poses a significant challenge to the real-time performance of an FVV system. Furthermore, the individuality of user interaction makes it difficult to serve multiple users simultaneously for a system with conventional architecture. In this paper, we novelly introduce a CNN-based view interpolation algorithm to synthesis dense virtual views in real time. Based on this, we also build an end-to-end live free-viewpoint system with a multi-user oriented streaming strategy. Our system can utilize a single edge server to serve multiple users at the same time without having to bring a large view synthesis load on the client side. We analyze the whole system and show that our approaches give the user a pleasant immersive experience, in terms of both visual quality and latency.

Yunqian Wen, Li Song, Bo Liu et al.

Because of the explosive growth of face photos as well as their widespread dissemination and easy accessibility in social media, the security and privacy of personal identity information becomes an unprecedented challenge. Meanwhile, the convenience brought by advanced identity-agnostic computer vision technologies is attractive. Therefore, it is important to use face images while taking careful consideration in protecting people's identities. Given a face image, face de-identification, also known as face anonymization, refers to generating another image with similar appearance and the same background, while the real identity is hidden. Although extensive efforts have been made, existing face de-identification techniques are either insufficient in photo-reality or incapable of well-balancing privacy and utility. In this paper, we focus on tackling these challenges to improve face de-identification. We propose IdentityDP, a face anonymization framework that combines a data-driven deep neural network with a differential privacy (DP) mechanism. This framework encompasses three stages: facial representations disentanglement, $ε$-IdentityDP perturbation and image reconstruction. Our model can effectively obfuscate the identity-related information of faces, preserve significant visual similarity, and generate high-quality images that can be used for identity-agnostic computer vision tasks, such as detection, tracking, etc. Different from the previous methods, we can adjust the balance of privacy and utility through the privacy budget according to pratical demands and provide a diversity of results without pre-annotations. Extensive experiments demonstrate the effectiveness and generalization ability of our proposed anonymization framework.

Yinzhi Cao, Zhanhao Chen, Song Li et al.

Timing attacks have been a continuous threat to users' privacy in modern browsers. To mitigate such attacks, existing approaches, such as Tor Browser and Fermata, add jitters to the browser clock so that an attacker cannot accurately measure an event. However, such defenses only raise the bar for an attacker but do not fundamentally mitigate timing attacks, i.e., it just takes longer than previous to launch a timing attack. In this paper, we propose a novel approach, called deterministic browser, which can provably prevent timing attacks in modern browsers. Borrowing from Physics, we introduce several concepts, such as an observer and a reference frame. Specifically, a snippet of JavaScript, i.e., an observer in JavaScript reference frame, will always obtain the same, fixed timing information so that timing attacks are prevented; at contrast, a user, i.e., an oracle observer, will perceive the JavaScript differently and do not experience the performance slowdown. We have implemented a prototype called DeterFox and our evaluation shows that the prototype can defend against browser-related timing attacks.