Flow correlation is a key technique for deanonymization attacks on Tor, while Tor can be used to achieve anonymous communication between Internet of Things (IoT) nodes in edge intelligence. Recent works treat flows as time series and use Convolutional Neural Network (CNN) to extract flow embeddings to improve the effectiveness of flow correlation attacks. However, unlike the consistent time intervals of data points in general time series, the arrival time intervals between flow packets are unequal, so the flow embeddings extracted are temporal inconsistent, which affects the effectiveness of flow correlation attacks. To address this challenge, we propose enhanced Flow Correlation attacks on Tor using Patching and Contrastive Learning (FlowCoPCL). First, FlowCoPCL uses a time-based patching mechanism to split the flow into patches of the same duration. The patch embeddings are extracted as the input of the CNN model to ensure the temporal consistency of the flow embeddings. Second, FlowCoPCL adapts the contrastive learning framework SimCLR to train the feature embedding networks, which enables the model to learn flow embeddings efficiently. The experimental results demonstrate that FlowCoPCL significantly outperforms existing flow correlation attacks on Tor, achieving over 99% True Positive Rate (TPR) at 10⁻⁵ False Positive Rate (FPR). Additionally, FlowCoPCL shows the robustness against website fingerprinting defenses.

Since different kinds of face forgeries leave similar forgery traces in videos, learning the common features from different kinds of forged faces would achieve promising generalization ability of forgery detection. Therefore, to accurately detect known forgeries while ensuring high generalization ability of detecting unknown forgeries, we propose an intra-inter network (IIN) for face forgery detection (FFD) in videos with continual learning. The proposed IIN mainly consists of three modules, i.e., intra-module, inter-module, and forged trace masking module (FTMM). Specifically, the intra-module is trained for each kind of face forgeries by supervised learning to extract special features, while the inter-module is trained by self-supervised learning to extract the common features. As a result, the common and special features of the different forgeries are decoupled by the two feature learning modules, and then the decoupled common features can be utlized to achieve high generalization ability for FFD. Moreover, the FTMM is deployed for contrastive learning to further improve detection accuracy. The experimental results on FaceForensic++ dataset demonstrate that the proposed IIN outperforms the state-of-the-arts in FFD. Also, the generalization ability of the IIN verified on DFDC and Celeb-DF datasets demonstrates that the proposed IIN significantly improves the generalization ability for FFD.