Xinghao Jiang

Jialin Yan, Yu Cheng, Zhaoxia Yin et al.

The rapid development of Artificial Intelligence Generated Content (AIGC) has made high-fidelity generated audio widely available across the Internet, driving the advancement of audio steganography. Benefiting from advances in deep learning, current audio steganography schemes are mainly based on encoder-decoder network architectures. While these methods guarantee a certain level of perceptual quality for stego audio, they typically face high computational cost and long implementation time, as well as poor anti-steganalysis performance. To address the aforementioned issues, we pioneer a Fixed Decoder Network-Based Audio Steganography with Adversarial Perturbation Generation (FGAS). Adversarial perturbations carrying a secret message are embedded into the cover audio to generate stego audio. The receiver only needs to share the structure and key of the fixed decoder network to accurately extract the secret message from the stego audio. In FGAS, we propose an Audio Adversarial Perturbation Generation (A2PG) strategy with an optional robust extension and design a lightweight fixed decoder. The fixed decoder guarantees reliable extraction of the hidden message, while adversarial perturbations are optimized to keep the stego audio perceptually and statistically close to the cover audio, thereby improving anti-steganalysis performance. The experimental results show that FGAS significantly improves stego audio quality, achieving an average PSNR gain of over 10 dB compared to SOTA methods. Furthermore, FGAS demonstrates strong robustness against common audio processing attacks. Moreover, FGAS exhibits superior anti-steganalysis performance across different relative payloads; under high-capacity embedding, it achieves a classification error rate about 2% higher, indicating stronger anti-steganalysis performance than current SOTA methods.

Qiang Xu, Shan Jia, Xinghao Jiang et al.

Distinguishing between computer-generated (CG) and natural photographic (PG) images is of great importance to verify the authenticity and originality of digital images. However, the recent cutting-edge generation methods enable high qualities of synthesis in CG images, which makes this challenging task even trickier. To address this issue, a joint learning strategy with deep texture and high-frequency features for CG image detection is proposed. We first formulate and deeply analyze the different acquisition processes of CG and PG images. Based on the finding that multiple different modules in image acquisition will lead to different sensitivity inconsistencies to the convolutional neural network (CNN)-based rendering in images, we propose a deep texture rendering module for texture difference enhancement and discriminative texture representation. Specifically, the semantic segmentation map is generated to guide the affine transformation operation, which is used to recover the texture in different regions of the input image. Then, the combination of the original image and the high-frequency components of the original and rendered images are fed into a multi-branch neural network equipped with attention mechanisms, which refines intermediate features and facilitates trace exploration in spatial and channel dimensions respectively. Extensive experiments on two public datasets and a newly constructed dataset with more realistic and diverse images show that the proposed approach outperforms existing methods in the field by a clear margin. Besides, results also demonstrate the detection robustness and generalization ability of the proposed approach to postprocessing operations and generative adversarial network (GAN) generated images.

Zixuan Chen, Hao Lin, Ke Xu et al.

Text-to-image (T2I) generation models can inadvertently produce not-safe-for-work (NSFW) content, prompting the integration of text and image safety filters. Recent advances employ large language models (LLMs) for semantic-level detection, rendering traditional token-level perturbation attacks largely ineffective. However, our evaluation shows that existing jailbreak methods are ineffective against these modern filters. We introduce GhostPrompt, the first automated jailbreak framework that combines dynamic prompt optimization with multimodal feedback. It consists of two key components: (i) Dynamic Optimization, an iterative process that guides a large language model (LLM) using feedback from text safety filters and CLIP similarity scores to generate semantically aligned adversarial prompts; and (ii) Adaptive Safety Indicator Injection, which formulates the injection of benign visual cues as a reinforcement learning problem to bypass image-level filters. GhostPrompt achieves state-of-the-art performance, increasing the ShieldLM-7B bypass rate from 12.5\% (Sneakyprompt) to 99.0\%, improving CLIP score from 0.2637 to 0.2762, and reducing the time cost by $4.2 \times$. Moreover, it generalizes to unseen filters including GPT-4.1 and successfully jailbreaks DALLE 3 to generate NSFW images in our evaluation, revealing systemic vulnerabilities in current multimodal defenses. To support further research on AI safety and red-teaming, we will release code and adversarial prompts under a controlled-access protocol.

Hui Liu, Wenya Wang, Kecheng Chen et al.

In zero-shot image recognition tasks, humans demonstrate remarkable flexibility in classifying unseen categories by composing known simpler concepts. However, existing vision-language models (VLMs), despite achieving significant progress through large-scale natural language supervision, often underperform in real-world applications because of sub-optimal prompt engineering and the inability to adapt effectively to target classes. To address these issues, we propose a Concept-guided Human-like Bayesian Reasoning (CHBR) framework. Grounded in Bayes' theorem, CHBR models the concept used in human image recognition as latent variables and formulates this task by summing across potential concepts, weighted by a prior distribution and a likelihood function. To tackle the intractable computation over an infinite concept space, we introduce an importance sampling algorithm that iteratively prompts large language models (LLMs) to generate discriminative concepts, emphasizing inter-class differences. We further propose three heuristic approaches involving Average Likelihood, Confidence Likelihood, and Test Time Augmentation (TTA) Likelihood, which dynamically refine the combination of concepts based on the test image. Extensive evaluations across fifteen datasets demonstrate that CHBR consistently outperforms existing state-of-the-art zero-shot generalization methods.

Xingkai Zheng, Xirui Li, Ke Xu et al.

As an emerging biological identification technology, vision-based gait identification is an important research content in biometrics. Most existing gait identification methods extract features from gait videos and identify a probe sample by a query in the gallery. However, video data contains redundant information and can be easily influenced by bagging (BG) and clothing (CL). Since human body skeletons convey essential information about human gaits, a skeleton-based gait identification network is proposed in our project. First, extract skeleton sequences from the video and map them into a gait graph. Then a feature extraction network based on Spatio-Temporal Graph Convolutional Network (ST-GCN) is constructed to learn gait representations. Finally, the probe sample is identified by matching with the most similar piece in the gallery. We tested our method on the CASIA-B dataset. The result shows that our approach is highly adaptive and gets the advanced result in BG, CL conditions, and average.

Laijin Meng, Xinghao Jiang, Zhenzhen Zhang et al.

Coverless steganography has been a great interest in recent years, since it is a technology that can absolutely resist the detection of steganalysis by not modifying the carriers. However, most existing coverless steganography algorithms select images as carriers, and few studies are reported on coverless video steganography. In fact, video is a securer and more informative carrier. In this paper, a novel coverless video steganography algorithm based on maximum Direct Current (DC) coefficients is proposed. Firstly, a Gaussian distribution model of DC coefficients considering video coding process is built, which indicates that the distribution of changes for maximum DC coefficients in a block is more stable than the adjacent DC coefficients. Then, a novel hash sequence generation method based on the maximum DC coefficients is proposed. After that, the video index structure is established to speed up the efficiency of searching videos. In the process of information hiding, the secret information is converted into binary segments, and the video whose hash sequence equals to secret information segment is selected as the carrier according to the video index structure. Finally, all of the selected videos and auxiliary information are sent to the receiver. Especially, the subjective security of video carriers, the cost of auxiliary information and the robustness to video compression are considered for the first time in this paper. Experimental results and analysis show that the proposed algorithm performs better in terms of capacity, robustness, and security, compared with the state-of-the-art coverless steganography algorithms.