The constantly changing landscape of the Internet presents a significant challenge in the detection and tracking of covert attackers and their sophisticated methods. To address this issue, various techniques, such as network flow watermarking (NFW) and traffic correlation, embed attack labels in data streams to identify attack pathways or aid post-analysis. However, existing solutions are often tailored to specific scenarios, resulting in lacking robustness, adaptability, and anonymity under non-cooperative or incomplete information heterogeneous environments. To this end, this paper proposesCorreFlow, a Transfer Learning (TL) based invisible network flow correlation framework utilizing time channel graph fingerprinting modulation. It considers the fragmentation and reassembly of data packets during transmission. In simple network environments,CorreFlowutilizes TL for rapid correlation across flows, enabling efficient linkage of related traffic segments. In complex heterogeneous network, where traditional correlation methods may fail due to encryption and variability, it leverages Inter Packet Delay (IPD) for encrypted flow matching and accurately identifies the optimal watermark point. Multiple experiments conducted on real network traffic and public datasets have demonstrated thatCorreFlowachieves highly efficient traffic correlation with minimal false positive rate, improved adaptability, and steganography. Specifically, it has achieved over 97.31% accuracy in various network environments and promotes network traffic correlation in open heterogeneous network environments from low correlation to 95%.

Deepfake video detection has drawn significant attention from researchers due to the security issues induced by deepfake videos. Unfortunately, most of the existing deepfake detection approaches have not competently modeled the natural structures and movements of human faces. In this paper, we formulate the deepfake video detection problem into a graph classification task, and propose a novel paradigm named Facial Action Dependencies Estimation (FADE) for deepfake video detection. We propose a Multi-Dependency Graph Module (MDGM) to capture abundant dependencies among facial action units, and extracts subtle clues in these dependencies. MDGM can be easily integrated into the existing frame-level detection schemes to provide significant performance gains. Extensive experiments demonstrate the superiority of our method against the state-of-the-art methods.