32 accepted papers
Recent advances in generative models have enabled the creation of high-fidelity human faces, yet constructing reliable virtual identities that preserve user privacy while supporting consistent and verifiable identity assignment remains challenging. In this paper, we propose a diffusion-based framewo
Multi-frame infrared small target detection suffers from extreme semantic paucity of targets and representation collapse due to overwhelming class imbalance, resulting in the persistent inability to accurately distinguish point-like targets from dynamic background clutter. To address these issues, w…
Despite multi-view clustering has achieved great progress in past decades, it is still a challenge to balance the effectiveness, efficiency, completeness and consistency of nonlinearly separable clustering for the data from different views. To address this challenge, we show that multi-view clusteri…
The proliferation of generative image models has revolutionized AIGC creation while amplifying concerns over content provenance and manipulation forensics. Existing methods are typically either unable to localize tampering or restricted to specific generative settings, limiting their practical utili
Learning on multi-view data is a fundamental task, which integrates the information from different views to improve the final performance. It is also a basic task for learning on the long-tailed data in real applications, followed by the downstream tasks, i.e., classification. The existing works for
With the rapid development of generative AI, image steganography has garnered widespread attention due to its unique concealment. Recent studies have demonstrated the practical advantages of Fixed Neural Network Steganography (FNNS), notably its ability to achieve stable information embedding and ex
Diffusion models have revolutionized image synthesis but raise concerns around content provenance and authenticity. Digital watermarking offers a means of tracing generated media, yet traditional schemes often introduce distributional shifts and degrade visual quality. Recent lossless methods embed…
Identifiable virtual face (IVF) generation aims to transform a user's original face into a virtual face for high utility privacy protection. The IVF is visually and statistically different from the original face, which can still be used for recognizing the user's identity. Despite the advantage, the…
Pancreatic NEuroendocrine Tumors (pNETs) are very rare endocrine neoplasms that account for less than 5% of all pancreatic malignancies, with an incidence of only 1–1.5 cases per 100,000. Early detection of pNETs is critical for improving patient survival, but the rarity of pNETs makes segmenting th…
The prevalence of AI-generated images has evoked concerns regarding the potential misuse of image generation technologies. In response, numerous detection methods aim to identify AI-generated images by analyzing generative artifacts. Unfortunately, most detectors quickly become obsolete with the dev…
Multi-view clustering aims to improve the final performance by taking advantages of complementary and consistent information of all views. In real world, data samples with partially available information are common and the issue regarding the clustering for incomplete multi-view data is inevitably r…
The exorbitant cost of training Large Language Models (LLMs) makes it essential to protect the models from illegal copying and unauthorized usage. Recent attempts at LLM protection utilize black-box watermarking schemes, which embed distinctive input-output mapping (i.e., trigger set) directly into…
Text-to-image (T2I) diffusion models exhibit impressive generation capabilities in recently studies. However, they are vulnerable to backdoor attacks, where model outputs are manipulated by malicious triggers. In this paper, we propose a novel input-level defense method, called Fine-grained Prompt S
The widespread use of face retouching on social media platforms raises concerns about the authenticity of face images. While existing methods focus on detecting face retouching, how to accurately recover the original faces from the retouched ones has yet to be answered. This paper introduces Face Re…
Embedding links in brand logos is a promising technology, which allows consumers to access the online information of products by capturing physical logo images. Previous physical data hiding methods primarily embed data within cover media in a global manner, making them ineffective for processing br…
Multi-view clustering has gained increasing attention by utilizing the complementary and consensus information across views. To alleviate the computation cost for the existing multi-view clustering approaches on datasets with large scales, studies based on anchor have been presented. Although extens…
The proliferation of digital images on the Internet has provided unprecedented convenience, but also poses significant risks of malicious theft and misuse. Digital watermarking has long been researched as an effective tool for copyright protection. However, it often falls short when addressing parti…
Text-to-image models have shown surprising performance in high-quality image generation, while also raising intensified concerns about the unauthorized usage of personal dataset in training and personalized fine-tuning. Recent approaches, embedding watermarks, introducing perturbations, and insertin…
Steganography is the art of hiding secret data into the cover media for covert communication. In recent years more and more deep neural network (DNN)-based steganographic schemes are proposed to train steganographic networks for secret embedding and recovery which are shown to be promising. Compared…
RAW files are the initial measurement of scene radiance widely used in most cameras, and the ubiquitously-used RGB images are converted from RAW data through Image Signal Processing (ISP) pipelines. Nowadays, digital images are risky of being nefariously manipulated. Inspired by the fact that innate…
Recently, the recognition of flat, nested, and discontinuous entities by a unified generative model framework has received increasing attention both in the research field and industry. However, the current generative NER methods force the entities to be generated in a predefined order, suffering fro…
Steganography is a technique for covert communication between two parties. With the rapid development of deep neural networks (DNN), more and more steganographic networks are proposed recently, which are shown to be promising to achieve good performance. Unlike the traditional handcrafted steganogra…
Deep neural network (DNN) watermarking is one of the main techniques to protect the DNN. Although various DNN watermarking schemes have been proposed, none of them is able to resist the DNN encryption. In this paper, we propose an encryption resistent DNN watermarking scheme, which is able to resist…
Recent advances in linguistic steganalysis have successively applied CNN, RNN, GNN and other efficient deep models for detecting secret information in generative texts. These methods tend to seek stronger feature extractors to achieve higher steganalysis effects. However, we have found through exper…
Data hiding with deep neural networks (DNNs) has experienced impressive successes in recent years. A prevailing scheme is to train an autoencoder, consisting of an encoding network to embed (or transform) secret messages in (or into) a carrier, and a decoding network to extract the hidden messages.…
Recent research has shown that deep learning based methods offer more accurate detection for image steganalysis than the traditional detection paradigm based on rich media models. Existing network architectures based on deep learning, however, stack more and more convolutional layers to increase loc…
Backdoor attacks are rapidly emerging threats to deep neural networks (DNNs). In the backdoor attack scenario, attackers usually implant the backdoor into the target model by manipulating the training dataset or training process. Then, the compromised model behaves normally for benign input yet make…
A large number of multi-party conversation scenarios exist in social networks, which have been seldom studied in the field of human-machine conversation. In this paper, we study a novel task of joint learning for addressee selection and response generation in multi-party conversations. Systems are e…
Backdoor attack against image classification task has been widely studied and proven to be successful, while there exist few researches on backdoor attack against vision-language models. In this paper, we explore backdoor attack towards image captioning models by poisoning training data. Assuming th…
Detection of manipulated face images has attracted a lot of interest recently. Various schemes have been proposed to tackle this challenging problem, where the patch-based approaches are shown to be promising. However, the existing patch-based approaches tend to treat different patches equally, whic…
Research shows that deep neural networks are vulnerable to back-door attacks. The backdoor network behaves normally on clean examples, but once backdoor patterns are attached to examples, back-door examples will be classified into the target class. In the previous backdoor attack schemes, backdoor p…
Since there is good potential for practical applications such as encrypted image authentication, content owner identification and privacy protection, reversible data hiding in encrypted image (RDHEI) has attracted increasing attention in recent years. In this paper, we propose and evaluate a new sep…