Liangwei Jiang

Liangwei Jiang, Jinluo Xie, Yecheng Huang et al.

Copy-move image forgery aims to duplicate certain objects or to hide specific contents with copy-move operations, which can be achieved by a sequence of manual manipulations as well as up-to-date deep generative network-based swapping. Its detection is becoming increasingly challenging for the complex transformations and fine-tuned operations on the tampered regions. In this paper, we propose a novel two-stream model, namely Multi-directional Similarity Network (MSN), to accurate and efficient copy-move forgery detection. It addresses the two major limitations of existing deep detection models in \textbf{representation} and \textbf{localization}, respectively. In representation, an image is hierarchically encoded by a multi-directional CNN network, and due to the diverse augmentation in scales and rotations, the feature achieved better measures the similarity between sampled patches in two streams. In localization, we design a 2-D similarity matrix based decoder, and compared with the current 1-D similarity vector based one, it makes full use of spatial information in the entire image, leading to the improvement in detecting tampered regions. Beyond the method, a new forgery database generated by various deep neural networks is presented, as a new benchmark for detecting the growing deep-synthesized copy-move. Extensive experiments are conducted on two classic image forensics benchmarks, \emph{i.e.} CASIA CMFD and CoMoFoD, and the newly presented one. The state-of-the-art results are reported, which demonstrate the effectiveness of the proposed approach.

Liangwei Jiang, Ruida Li, Zhifeng Zhang et al.

This paper aims to bring fine-grained expression control while maintaining high-fidelity identity in portrait generation. This is challenging due to the mutual interference between expression and identity: (i) fine expression control signals inevitably introduce appearance-related semantics (e.g., facial contours, and ratio), which impact the identity of the generated portrait; (ii) even coarse-grained expression control can cause facial changes that compromise identity, since they all act on the face. These limitations remain unaddressed by previous generation methods, which primarily rely on coarse control signals or two-stage inference that integrates portrait animation. Here, we introduce EmojiDiff, the first end-to-end solution that enables simultaneous control of extremely detailed expression (RGB-level) and high-fidelity identity in portrait generation. To address the above challenges, EmojiDiff adopts a two-stage scheme involving decoupled training and fine-tuning. For decoupled training, we innovate ID-irrelevant Data Iteration (IDI) to synthesize cross-identity expression pairs by dividing and optimizing the processes of maintaining expression and altering identity, thereby ensuring stable and high-quality data generation. Training the model with this data, we effectively disentangle fine expression features in the expression template from other extraneous information (e.g., identity, skin). Subsequently, we present ID-enhanced Contrast Alignment (ICA) for further fine-tuning. ICA achieves rapid reconstruction and joint supervision of identity and expression information, thus aligning identity representations of images with and without expression control. Experimental results demonstrate that our method remarkably outperforms counterparts, achieves precise expression control with highly maintained identity, and generalizes well to various diffusion models.