Identity-preserving models have led to notable progress in generating personalized content. Unfortunately, such models also exacerbate risks when misused, for instance, by generating threatening content targeting specific individuals. This paper introduces the Attribute Misbinding Attack, a novel method that poses a threat to identity-preserving models by inducing them to produce Not-Safe-For-Work (NSFW) content. The attack's core idea involves crafting benign-looking textual prompts to circumvent text-filter safeguards and leverage a key model vulnerability: flawed attribute binding that stems from its internal attention bias. This results in misattributing harmful descriptions to a target identity and generating NSFW outputs. To facilitate the study of this attack, we present the Misbinding Prompt evaluation set, which examines the content generation risks of current state-of-the-art identity-preserving models across four risk dimensions: pornography, violence, discrimination, and illegality. Additionally, we introduce the Attribute Binding Safety Score (ABSS), a metric for concurrently assessing both content fidelity and safety compliance. Experimental results show that our Misbinding Prompt evaluation set achieves a 5.28 % higher success rate in bypassing five leading text filters (including GPT-4o) compared to existing main-stream evaluation sets, while also demonstrating the highest proportion of NSFW content generation. The proposed ABSS metric enables a more comprehensive evaluation of identity-preserving models by concurrently assessing both content fidelity and safety compliance.

Document Presentation Attack is an anti-forensic operation that conceals the forgery traces of image manipulation in the digital domain. Existing document presentation attack detection (DPAD) methods show unsatisfactory performance under samples with different contents and qualities. In this work, we focus on the DPAD task on screen-recapturing channel and exploit the prior knowledge of distortion (i.e., moire pattern) in the spectral domain to address these limitations. We propose a frequency-domain moir ´ e´ augmentation (FMAG) strategy that enhances the spectral components contributed to the moire distortion, improving the generalization ´ performance under different document contents. We devise the mask moire frequency modeling (M ´ 2FM) scheme to reconstruct the moire-related spectral components in low-quality samples under the guidance of the spectral distortion model and a pre-trained DPAD ´ classifier. To evaluate the generalization performance, we collect the diverse Screen Recaptured Document Image Dataset with 162 different document contents (SRDID162) consisting of 162 genuine document images, as well as 2592 low and high-quality recaptured document images, respectively. Our experimental protocol involves training with high-quality ID images and testing with SRDID162 dataset of diverse contents and image qualities. Compared to a SOTA data augmentation approach for recaptured natural images, our FMAG & M2FM approach achieves a significant improvement of 49.15% or 22.50 percentage points in average EER on the generic deep learning backbones. The data and code of this work will be available at Github

The molecular subtype of breast cancer is significant for patients' treatment and prognosis. The application of multi-sequence MRI technology provides a new non-invasive diagnostic method, which can more accurately assess the vascular status of tumors and reveal fine structures. However, providing interpretable classification results remains a challenge. Recently, although many convolutional neural network (CNN) and fine-grained classification methods based on MRI inputs have been proposed. However, most of these methods operate in a âblack-boxâ without a detailed explanation of the intermediate processes, resulting in a lack of interpretability of the breast cancer classification process. To address this problem, we proposes a multi-sequence MRI-based hierarchical expert diagnostic method for the molecular subtype of breast cancer. With the strong differentiation module, this method first identifies enhanced features in breast tumors, ensuring that the subsequent classification process is precisely focused on the lesion features. In addition, inspired by the co-diagnosis of multiple experts in clinical diagnosis, we set up a mechanism of collaborative diagnostic corrective learning by hierarchical experts to provide an interpretable classification process. Compared with previous studies, the framework learns features with a strong distinguishing ability for breast tumor classification. Specifically, multiple experts corrected each other's learning to give more accurate and interpretable classification results, significantly improving clinical diagnosis's practical value. We conducted extensive experiments on a breast dataset and compared it quantitatively with other methods, and we achieved the best performance in terms of accuracy (0.889) and F1 Score (0.893). We make the code public on GitHub: https://github.com/yanfangHao/HED.

Diffusion models have made significant strides in visual content generation but also raised increasing demands on generated image detection. Existing detection methods have achieved considerable progress, but they usually suffer a significant decline in accuracy when detecting images generated by an unseen diffusion model. In this paper, we seek to address the generalizability of generated image detectors from the perspective of hard sample classification. The basic idea is that if a classifier can distinguish generated images that closely resemble real ones, then it can also effectively detect less similar samples, potentially even those produced by a different diffusion model. Based on this idea, we propose Diffusion Reconstruction Contrastive Learning (DRCT), a universal framework to enhance the generalizability of the existing detectors. DRCT generates hard samples by high-quality diffusion reconstruction and adopts contrastive training to guide the learning of diffusion artifacts. In addition, we have built a million-scale dataset, DRCT-2M, including 16 types diffusion models for the evaluation of generalizability of detection methods. Extensive experimental results show that detectors enhanced with DRCT achieve over a 10% accuracy improvement in cross-set tests. The code, models, and dataset will soon be available at https://github.com/beibuwandeluori/DRCT.

This paper presents a summary of the DeepFake Game Competition (DFGC) 20211. DeepFake technology is developing fast, and realistic face-swaps are increasingly deceiving and hard to detect. At the same time, DeepFake detection methods are also improving. There is a two-party game between DeepFake creators and detectors. This competition provides a common platform for benchmarking the adversarial game between current state-of-the-art DeepFake creation and detection methods. In this paper, we present the organization, results and top solutions of this competition and also share our insights obtained during this event. We also release the DFGC-21 testing dataset collected from our participants to further benefit the research community2.

Recently, various Deepfake detection methods have been proposed, and most of them are based on convolutional neural networks (CNNs). These detection methods suffer from overfitting on the source dataset and do not perform well on cross-domain datasets which have different distributions from the source dataset. To address these limitations, a new method named FeatureTransfer is proposed in this paper, which is a two-stage Deepfake detection method combining with transfer learning. Firstly, The CNN model pretrained on a third-party large-scale Deepfake dataset can be used to extract the more transferable feature vectors of Deepfake videos in the source and target domains. Secondly, these feature vectors are fed into the domain-adversarial neural network based on backpropagation (BP-DANN) for unsupervised domain adaptive training, where the videos in the source domain have real or fake labels, while the videos in the target domain are unlabelled. The experimental results indicate that the proposed method FeatureTransfer can effectively solve the overfitting problem in Deepfake detection and greatly improve the performance of cross-dataset evaluation.