Fingerprint Presentation Attack Detection (PAD) is crucial for ensuring the reliability of fingerprint recognition systems. Mainstream methods typically frame PAD as a binary classification task. Although they achieve commendable results, these approaches often fail on unseen spoofs. This is because their decision boundaries rely heavily on trainset spoofs, leading to poor generalization. To address this issue, we reformulate PAD as an anomaly detection problem and propose the Fixed-Prototype PAD method. First, we introduce Fixed-Prototype Contrastive Learning (FPCL) for extracting PAD features. FPCL clusters the features of live fingerprints near a fixed prototype while pushing the features of spoof fingerprints away. We then design a Fixed-Prototype Anomaly Detection method to replace binary classification for decision-making. Specifically, this involves comparing the distance between test fingerprint features and the fixed live prototype to detect spoofs. Our method eliminates the dependency on the trainset spoofs during decision-making, thereby enhancing generalization capability. Experimental results demonstrate that our approach achieves state-of-the-art performance on the LivDet 2019 and LivDet 2021 benchmarks.

The advancement of Generative Adversarial Networks (GANs) and diffusion models significantly enhances the realism of synthetic images, driving progress in image processing and creative design. However, this progress also necessitates the development of effective detection methods, as synthetic images become increasingly difficult to distinguish from real ones. This difficulty leads to societal issues, such as the spread of misinformation, identity theft, and online fraud. While previous detection methods perform well on public benchmarks, they struggle with our benchmark, FakeART, particularly when dealing with the latest models and cross-domain tasks (e.g., photo-to-painting). To address this challenge, we develop a new synthetic image detection technique based on color distribution. Unlike real images, synthetic images often exhibit uneven color distribution. By employing color quantization and restoration techniques, we analyze the color differences before and after image restoration. We discover and prove that these differences closely relate to the uniformity of color distribution. Based on this finding, we extract effective color features and combine them with image features to create a detection model with only 1.4 million parameters. This model achieves state-of-the-art results across various evaluation benchmarks, including the challenging FakeART dataset.

Structural information in images is crucial for aesthetic assessment, and it is widely recognized in the artistic field that imitating the structure of other works significantly infringes on creators’ rights. The advancement of diffusion models has led to AI-generated content imitating artists’ structural creations, yet effective detection methods are still lacking. In this paper, we define this phenomenon as "structural infringement" and propose a corresponding detection method. Additionally, we develop quantitative metrics and create manually annotated datasets for evaluation: the SIA dataset of synthesized data, and the SIR dataset of real data. Due to the current lack of datasets for structural infringement detection, we propose a new data synthesis strategy based on diffusion models and LLM, successfully training a structural infringement detection model. Experimental results show that our method can successfully detect structural infringements and achieve notable improvements on annotated test sets.

Vision-language models (VLMs) often struggle with compositional reasoning due to insufficient high-quality image-text data. To tackle this challenge, we propose a novel block-based diffusion approach that automatically generates counterfactual datasets without manual annotation. Our method utilizes large language models to identify entities and their spatial relationships. It then independently generates image blocks as "puzzle pieces" coherently arranged according to specified compositional rules. This process creates diverse, high-fidelity counterfactual image-text pairs with precisely controlled variations. In addition, we introduce a specialized loss function that differentiates inter-set from intra-set samples, enhancing training efficiency and reducing the need for negative samples. Experiments demonstrate that fine-tuning VLMs with our counterfactual datasets significantly improves visual reasoning performance. Our approach achieves state-of-the-art results across multiple benchmarks while using substantially less training data than existing methods.

Recent advances in generative modeling have enabled the synthesis of high-quality artistic images. Nevertheless, systematic evaluation of generative models from an aesthetic standpoint is still lacking, which hinders progress in artistic image synthesis. Existing evaluation metrics, such as Fréchet Inception Distance (FID) and CMMD, struggle with aesthetic assessment: they rely on pretrained visual features that overlook nuanced artistic attributes and employ distance functions ill-suited for modeling the diverse, multi-modal distribution of artistic styles. To address these limitations, we propose ArtFRD, a metric specifically designed for generative aesthetic evaluation. Grounded in aesthetic theory, ArtFRD extracts visual features along four key aesthetic dimensions-brushstroke, composition, lighting, and color-to capture fine-grained artistic properties. To model the multi-modal nature of artistic styles, we adopt a Gaussian Mixture Model assumption and derive an efficient approximation of the Fisher-Rao distance, which serves as the final evaluation score. Extensive experiments demonstrate that ArtFRD aligns significantly better with human aesthetic judgments than existing metrics, even across a wide range of artistic styles. These results highlight its potential as a robust and interpretable foundation for future research in generative aesthetic evaluation.

Fingerprint Presentation Attack Detection (PAD) is a crucial step in automatic fingerprint identification systems, which safeguards users from unauthorized malicious access. However, current presentation attack (i.e. spoof) techniques can forge intricate details of fingerprints (such as sweat holes), which makes the artifact evidence harder to detect. In this paper, we propose a novel PAD method from the perspective of decomposition to highlight the artifact evidence in each constituent element. Specifically, we utilize the fingerprint enhancement to decompose the fingerprint into the ridge region and the edge region. We observe that artifact evidence mainly exists in the gradient field within the ridge region, while it primarily resides in the spatial domain within the edge region. Then we propose an Orientation-Based Central Difference Convolution (OB-CDC) layer to prioritize gradient variations along the ridge direction. To further enhance robustness, we propose a Minutia Patches Random Rotation (MPRR) operation to disrupt the identity information of the fingerprint while preserving the artifact evidence. By integrating these techniques, we propose a two-stream network called Presentation-Attack-Detection-with-Region-Decomposition-Network (PADRD-Net) which integrates the processed feature of the ridge region and the edge region through a halfway fusion ResNet-18 structure. Experimental results on the LivDet 2021 dataset show that our proposed PADRD-Net can achieve 20.39% on BPCER@APCER = 1% and 87.12% on TDR@FDR = 1%, significantly outperforms the state-of-the-art. We also achieve outstanding performance in both the cross-sensor scenario and the cross-sensor and cross-material scenario. Extensive ablation studies and analysis experiments further indicate the effectiveness and robustness of our method.

Fingerprint recognition is a crucial biometric technology extensively used in identity verification, including areas like criminal investigations, security systems, and biometric authentication. This technology encounters greater challenges when dealing with incomplete fingerprint images, especially those with significant background noise or substantial portions of the fingerprint missing. Existing incomplete fingerprint recognition technologies struggle with extensive data loss, primarily due to the significant reduction and difficulty in extracting usable features from incomplete fingerprint images. Current image processing methods or deep learning models are unable to comprehensively reconstruct fingerprint features with limited information. To address these challenges, we introduce the Finger Recovery Transformer (FingerRT), an innovative network specifically designed for recovering incomplete fingerprint information. FingerRT can simultaneously complete ambient noise cancellation and fingerprint feature information recovery, resulting in a complete and clean fingerprint image. FingerRT combines the most critical feature information in fingerprints, directional field, and minutiae, as supervision information. FingerRT inherits the denoising ability of the fingerprint enhancement networks and the powerful generative ability of the Vision Transformer architecture, enabling high-quality and robust fingerprint information recovery. By imposing constraints at multiple levels, including fingerprint features, fingerprint images, and multi-stage generation, FingerRT can complete fingerprint information accurately and effectively. Experiments demonstrate that FingerRT significantly enhances fingerprint recognition accuracy after recovery across various fingerprint datasets, including rolled, snapped, and latent fingerprints.

The security of Automated Fingerprint Identification Systems (AFIS) heavily relies on the performance of the Fingerprint Presentation Attack Detection (FPAD) methods. However, the difficulty of FPAD lies in how to have strong robustness and generalization to unseen spoof fingerprints. To address this issue, we propose a novel FPAD framework with tailored Supervised Contrastive Learning (SupCon) and KNN-based OOD detection (KNN-OOD) method. We tailor the SupCon to better constrain the distribution of learned features by incorporating dynamic feature and label queues into SupCon and actively mining positive samples from the queues. In FPAD, we consider fingerprints with the same PAD label as intra-class, while those with different labels as inter-class. The tailored SupCon makes intra-class features more compact and inter-class features more dispersed. Utilizing the compact live fingerprint feature distribution, during the testing phase, we employ KNNOOD as an alternative to commonly used classification approaches. Since this approach does not rely on the distribution of trainset spoof fingerprints, it consistently achieves outstanding results even for unseen spoof fingerprints. Experiment results demonstrate that our proposed FPAD-SupCon framework achieves state-of-the-art performance on LivDet 2019 and LivDet 2021 datasets.

Latent fingerprint segmentation is a crucial process in contemporary biometric systems utilized in criminal investigations and security applications. Accurately segmenting the fingerprint region from the background noise and artifacts, which can be challenging due to the complexity of the surrounding environment, is the primary goal of this process. Although various methodologies, including binarization-based, texture-based, and deep learning-based segmentation approaches have been proposed, they are often limited by environmental noise and a scarcity of annotated data, resulting in a low segmentation accuracy rate. In this paper, we propose FingerSTR (Finger Segmentation Transformer), a fully Transformer-based latent fingerprint segmentation network, and introduce a new teacher-student training methodology to achieve more precise and robust segmentation results without requiring manual annotation. Based on experimental results of latent fingerprint database NIST SD27, FingerSTR surpasses both deep-learning algorithms and handcraft methods, achieving state-of-the-art performance in the latent fingerprint segmentation task.

Latent fingerprints play a vital role in forensic investigations. However, accurately estimating their orientation field can be challenging due to complex noise or overlapping fingerprint regions. In this paper, we propose a method to identify and correct these regions in the orientation field estimation. Specifically, our method comprises two networks: the first is an orientation field estimation network that outputs the initial orientation field, segment, and quality map, which determines the low-quality regions, including overlapping fingerprints and unclear ridge areas. The second network refills the orientation field in low-quality regions using inpainting techniques. This effectively handles unclear ridges and overlapping fingerprints, which can disrupt orientation field estimation. We assess our method using the NIST SD 27 dataset and demonstrate superior performance compared to existing state-of-the-art latent orientation field estimation methods, achieving the average root mean square deviation of 11.20.

Detecting digital face manipulation has attracted extensive attention due to fake media's potential risks to the public. However, recent advances have been able to reduce the forgery signals to a low magnitude. Decomposition, which reversibly decomposes an image into several constituent elements, is a promising way to highlight the hidden forgery details. In this paper, we investigate a novel 3D decomposition based method that considers a face image as the production of the interaction between 3D geometry and lighting environment. Specifically, we disentangle a face image into four graphics components including 3D shape, lighting, common texture, and identity texture, which are respectively constrained by 3D morphable model, harmonic reflectance illumination, and PCA texture model. Meanwhile, we build a fine-grained morphing network to predict 3D shapes with pixel-level accuracy to reduce the noise in the decomposed elements. Moreover, we propose a composition search strategy that enables an automatic construction of an architecture to mine forgery clues from forgery-relevant components. Extensive experiments validate that the decomposed components highlight forgery artifacts, and the searched architecture extracts discriminative forgery features. Thus, our method achieves the state-of-the-art performance.

Detecting digital face manipulation has attracted extensive attention due to fake media’s potential harms to the public. However, recent advances have been able to reduce the forgery signals to a low magnitude. Decomposition, which reversibly decomposes an image into several constituent elements, is a promising way to highlight the hidden forgery details. In this paper, we consider a face image as the production of the intervention of the underlying 3D geometry and the lighting environment, and decompose it in a computer graphics view. Specifically, by disentangling the face image into 3D shape, common texture, identity texture, ambient light, and direct light, we find the devil lies in the direct light and the identity texture. Based on this observation, we propose to utilize facial detail, which is the combination of direct light and identity texture, as the clue to detect the subtle forgery patterns. Besides, we highlight the manipulated region with a supervised attention mechanism and introduce a two-stream structure to exploit both face image and facial detail together as a multi-modality task. Extensive experiments indicate the effectiveness of the extra features extracted from the facial detail, and our method achieves the state-of-the-art performance.

This letter proposes a new hyperspectral classification method that fuses superpixel spectral metrics and joint sparse representation (JSR), which is termed as superpixel spectral metrics representation (SSMR). Recently, superpixel segmentation has proven to be a powerful tool to exploit the spatial information of hyperspectral images (HSIs), since the size and shape of each superpixel can be adaptively changed in different structural textures. Moreover, spectral information divergence (SID) has superiority compared to other distance-based similarity measures, particularly when using with a JSR classifier. Taking the aforemementioned advantages into account, superpixel segmentation, SID, and JSR are availably combined to effectively utilize the spectralspatial information of the HSI. The proposed SSMR method includes the following main steps. First, superpixel segmentation is utilized to divide the original map into several superpixels. Second, similarity metric SID among test samples in all superpixels and training samples are calculated. Next, the JSR model is employed to obtain the reconstruction residuals of each class. Then, a regularization parameter λ is introduced to attain balance between JSR and SID. Finally, pixel's label is determined by the minimal total residual. Experimental results on the Indian Pines dataset show better performance than several well-known classification methods.