Wanqing Tu

Jiyu Liu, Yong Huang, Yanzhao Lu et al.

This paper studies the creation of textual descriptions of user activities and interactions on smartphones. Our approach of referring to encrypted mobile traffic exceeds traditional smartphone activity classification methods in terms of model scalability and output readability. The paper addresses two obstacles to the realization of this idea: the semantic gap between traffic features and smartphone activity captions, and the lack of textually annotated traffic data. To overcome these challenges, we introduce a novel smartphone activity captioning system, called T2T (Traffic-to-Text). T2T consists of a flow feature encoder that converts low-level traffic characteristics into meaningful latent features and a caption decoder to yield readable transcripts of smartphone activities. In addition, T2T achieves the automatic textual annotation of mobile traffic by feeding synchronized screen capture videos into the Qwen-VL-Max vision-language model, and proposing multi-stage losses for effective cross-model training. We evaluate T2T on 40,000 traffic-description pairs collected in two real-world environments, involving 8 smartphone users and 20 mobile apps. T2T achieves a BLEU-4 score of 58.1, a METEOR score of 38.3, a ROUGE-L score of 70.5, and a CIDEr score of 108.7. The quantitative and qualitative analyses show that T2T can generate semantically accurate captions that are comparable to the vision-language model.

Yong Huang, Yanzhao Lu, Mingyang Chen et al.

In video conferencing, human faces serve as the primary visual focal points, playing multifaceted roles that enhance visual communication and emotional connection. However, we argue that a human face is also a side channel, which can unwittingly leak on-screen information through online video feeds. To demonstrate this, we conduct feasibility studies, which reveal that, illuminated by both ambient light and light emitted from displays, the human face can reflect optical variations of different on-screen content. The paper then proposes FaceTell, a novel side-channel attack system that eavesdrops on fine-grained application activities from pervasive yet subtle facial reflections during video conferencing. We implement FaceTell in a real-world testbed with three different brands of laptops and four mainstream video conferencing platforms. FaceTell is then evaluated with 24 human subjects across 13 unique indoor environments. With more than 12 hours of video data, FaceTell achieves a high accuracy of 99.32% for eavesdropping on 28 popular applications and is resilient to many practical impact factors. Finally, potential countermeasures are proposed to mitigate this new attack.