In recent years, large language models (LLM) have progressed rapidly, leading to growing concerns about the proliferation of difficult-to-distinguish AI-generated content. This has given rise to a range of issues, including fake news, academic fraud, phishing emails, posing significant dangers across various domains. However, current machine-generated text (MGT) detection methods still face challenges, including the need to access model's output logits or losses, which makes it unable to adapt to black-box scenarios in the real world, and difficult to deploy models with large parameter sizes. Therefore, we propose a compression-based lightweight network for MGT detection that leverages the ability of lossless compression to effectively extract features between categories. With fewer parameters, our framework achieves state-of-the-art performance in MGT detection under black box conditions. Experiments demonstrate that our approach performs exceptionally well on both Chinese and English datasets. Specifically, our method achieves a fulltext detection accuracy of 99.5%, surpassing the previous SOTA method.

In video coding, inter prediction aims to reduce temporal redundancy by using previously encoded frames as references. The quality of reference frames is crucial to the performance of inter prediction. This paper presents a deep reference frame generation method to optimize the inter prediction in Versatile Video Coding (VVC). Specifically, reconstructed frames are sent to a well-designed frame generation network to synthesize a picture similar to the current encoding frame. The synthesized picture serves as an additional reference frame inserted into the reference picture list (RPL) to provide a more reliable reference for subsequent motion estimation (ME) and motion compensation (MC). The frame generation network employs optical flow to predict motion precisely. Moreover, an optical flow reorganization strategy is proposed to enable bi-directional and uni-directional predictions with only a single network architecture. To reasonably apply our method to VVC, we further introduce a normative modification of the temporal motion vector prediction (TMVP). Integrated into the VVC reference software VTM-15.0, the deep reference frame generation method achieves coding efficiency improvements of 5.22%, 3.61%, and 3.83% for the Y component under random access (RA), low delay B (LDB), and low delay P (LDP) configurations, respectively. The proposed method has been discussed in Joint Video Exploration Team (JVET) meeting and is currently part of Exploration Experiments (EE) for further study.

Text steganography is becoming increasingly secure by eliminating the distribution discrepancy between normal and stego text. On the other hand, the existing cross-entropy-based steganalysis models struggle to distinguish subtle distribution differences and lack robustness regarding confusable samples. To enhance steganalysis accuracy on hard-to-detect samples, this paper draws on contrastive learning to design a text steganalysis framework incorporating supervised contrastive loss into the training process. This framework improves feature representation by pushing apart embeddings from different classes while pulling closer embeddings from the same class. The experimental results show that our method makes remarkable improvement compared to the four baseline models. Additionally, as the embedding rate increases, our method's advantages become increasingly apparent, with maximum improvements of 13.98%, 12.47%, and 13.65% over the baseline methods across three common linguistic steganalysis datasets, Twitter, IMDB, and News, respectively. Our code is available at https://github.com/Katelin-glt/SCL-Stega https://github.com/katelin-glt/SCL-Stega.

In this paper, we propose a deep reference frame generation method that aims to enhance bi-direction inter prediction under random access configuration in the latest video coding standard, Versatile Video Coding. Specifically, a pair of neighboring reconstructed frames are selected from decoded picture buffer and put into an optical-flow-based interpolation network to synthesize a new frame, similar to the current to-be-coded frame. Subsequently, this synthesized frame is incorporated into two-sided picture reference lists as additional reference frames. The proposed method is employed in both the encoding and decoding processes to eliminate bitstream signaling for supplementary information. The Small Ad-hoc Deep-Learning Library is utilized for implementing the proposed method. Experimental results demonstrate 3.67%/7.34%/6.51% coding efficiency improvements for Y/U/V components under the random access configuration when compared to the Versatile Video Coding NNVC reference software VTM-11_NNVC-5.0.

Deep neural network (DNN)-based methods have demonstrated enormous potential for Versatile Video Coding (VVC) inter prediction enhancement. However, due to their typically high computational complexity, implementing them in practical applications can be challenging. In this paper, we propose a lightweight deep reference frame interpolation network to enhance bi-prediction with low complexity. Specifically, given a pair of bi-directional reconstructed frames, first, we down-sample input frames to reduce the complexity before feeding them into the optical flow estimation network. Then the optical flows are utilized to warp extracted features at three different levels. The warped features are fused to generate the output intermediate frame. The additional reference frame is inserted into the reference picture lists to provide an additional reliable reference candidate. In contrast to previous efforts, the proposed method aims at achieving the trade-off between performance and complexity while maintaining a complexity of about 64 kMACs/pix. Experimental results demonstrate that our method achieves 1.82%/2.43%/2.02% coding efficiency improvements for Y/U/V components under random access (RA) configuration compared to the latest NNVC standard software VTM-11.0_NNVC-5.0.

In video coding, bi-directional inter prediction aims to remove temporal redundancy via two-sided previously coded frames as reference. High-quality reference frames are essential to reduce the prediction residuals and improve coding efficiency performance. In this paper, we propose a deep learning-based reference frame interpolation method to enhance bi-prediction by introducing a synthetic frame to reference picture lists. Specifically, reconstructed frames are fed into a well-designed interpolation and filtering network to synthesize a picture which can be regarded as an additional reference of to-be-coded frame. Then, the picture is inserted at the appropriate place in reference picture lists to provide a more reliable reference for subsequent motion estimation and motion compensation. Experimental results show that the proposed method achieves 2.03%/6.96%/6.40% coding efficiency improvements for Y/U/V components under random access configuration, when compared with the VVC reference software VTM-15.0.