Mesh watermark embeds secret messages in 3D meshes and decodes the message from watermarked meshes for ownership verification. Current watermarking methods directly hide secret messages in vertex and face sets of meshes. However, mesh is a discrete representation that uses vertex and face sets to describe a continuous signal, which can be discretized in other discrete representations with different vertex and face sets. This raises the question of whether the watermark can still be verified on the different discrete representations of the watermarked mesh. We conduct this research in an attack-then-defense manner by proposing a novel function space mesh watermark removal attack FuncEvade and then mitigating it through function space mesh watermarking FuncMark. In detail, FuncEvade generates a different discrete representation of a watermarked mesh by extracting it from the signed distance function of the watermarked mesh. We observe that the generated mesh can evade ALL previous watermarking methods. FuncMark mitigates FuncEvade by watermarking signed distance function through message-guided deformation. Such deformation can survive isosurfacing and thus be inherited by the extracted meshes for further watermark decoding. Extensive experiments demonstrate that FuncEvade achieves 100% evasion rate among all previous watermarking methods while achieving only 0.3% evasion rate on FuncMark. Besides, our FuncMark performs similarly on other metrics compared to state-of-the-art mesh watermarking methods.

The primary obstacle in realizing the full potential of finger crease biometrics is the accurate identification of deformed knuckle patterns, often resulting from completely contactless imaging. Current methods struggle significantly with this task, yet accurate matching is crucial for applications ranging from forensic investigations, such as child abuse cases, to surveillance and mobile security. To address this challenge, our study introduces the largest publicly available dataset of deformed knuckle patterns, comprising 805,768 images from 351 subjects. We also propose a novel framework to accurately match knuckle patterns, even under severe pose deformations, by recovering interpretable knuckle crease keypoint feature templates. These templates can dynamically uncover graph structure and feature similarity among the matched correspondences. Our experiments, using the most challenging protocols, illustrate significantly outperforming results for matching such knuckle images. For the first time, we present and evaluate a theoretical model to estimate the uniqueness of 2D finger knuckle patterns, providing a more interpretable and accurate measure of distinctiveness, which is invaluable for forensic examiners in prosecuting suspects.

The remarkable progress in neural-network-driven visual data generation, especially with neural rendering techniques like Neural Radiance Fields and 3D Gaussian splatting, offers a powerful alternative to GANs and diffusion models. These methods can generate high-fidelity images and lifelike avatars, highlighting the need for robust detection methods. However, the lack of any large dataset containing images from neural rendering methods becomes a bottleneck for the detection of such sophisticated fake images. To address this limitation, we introduce NeuroRenderedFake, a comprehensive benchmark for evaluating emerging fake image detection methods. Our key contributions are threefold: (1) A large-scale dataset of fake images synthesized using state-of-the-art neural rendering techniques, significantly expanding the scope of fake image detection beyond generative models; (2) A cross-domain evaluation protocol designed to assess the domain gap and common artifacts between generative and neural rendering-based fake images; and (3) An in-depth spectral energy analysis that reveals how frequency domain characteristics influence the performance of fake image detectors. We train representative detectors, based on spatial, spectral, and multimodal architectures, on fake images generated by both generative and neural rendering models. We evaluate these detectors on 15 groups of fake images synthesized by cutting-edge neural rendering models, generative models, and combined methods that can exhibit artifacts from both domains. Additionally, we provide insightful findings through detailed experiments on degraded fake image detection and the impact of spectral features, aiming to advance research in this critical area.

Contactless 3D fingerprint identification systems have emerged to provide more accurate and hygienic alternatives to contact-based conventional systems that acquire hundreds of millions of fingerprints everyday. However, the intricate process of acquiring 3D fingerprints presents a significant challenge, acting as a key barrier to fully unlocking the potential of 3D fingerprint biometrics. This paper introduces a novel framework to directly recover corresponding 3D minutiae template from a single contactless 2D fingerprint image. Billions of contact-based fingerprints have been acquired and employed everyday for e-governance and other applications. Seamless adoption of contactless 3D fingerprint technologies also requires advanced capabilities to accurately match 3D fingerprints with respective 2D fingerprint templates, which is currently missing in existing literature. We therefore introduce novel capabilities to accurately align minutiae templates in 3D spaces and enable compensation for the unknown perspective transformation. This capability significantly enhances the ability to accurately match 3D to 3D and 3D to 2D fingerprint templates. Furthermore, we introduce a new approach to synthesizing realistic contactless fingerprint images, resulting in the generation of a large synthetic database complete with corresponding 3D ground truths of minutiae points. Finally, we provide a detailed theoretical analysis of formulation for the uniqueness of recovered 3D minutiae templates, providing a theoretical justification for the superiority of such 3D minutiae templates over their 2D counterparts.

Billions of contact-based fingerprint images have been acquired in large databases. Contactless 2D fingerprint identification systems have emerged to provide more hygienic and secured alternatives and are highly sought under the current pandemic. The success of such an alternative requires high match accuracy, not just for the contactless-to-contactless but also for the contactless-to-contact-based matching, which is currently below expectations for large-scale deployments. We introduce a new approach to advance such expectations on match accuracy and also to address privacy-related concerns, e.g., recent GDPR regulations, in the acquisition of very large databases. This paper introduces a novel approach for accurately synthesizing multi-view contactless 3D fingerprints to develop a very large-scale multi-view fingerprint database, and corresponding contact-based fingerprint database. A unique advantage of our approach is the simultaneous availability of much-needed ground truth labels and alleviation of laborious and often prone to erroneous tasks performed by human labeling. We also introduce a new framework that can not only accurately match contactless to contact-based images but also contactless to contactless images, as both of these capabilities are simultaneously required to advance contactless fingerprint technologies. Our rigorous experimental results presented in this paper, both for within-database and cross-database experiments, illustrate outperforming results to simultaneously meet both of these expectations and validate the effectiveness of the proposed approach.

Accurate detection of the fake but photorealistic images is one of the most challenging tasks to address social, biometrics security and privacy related concerns in our community. Earlier research has underlined the existence of spectral domain artifacts in fake images generated by powerful generative adversarial network (GAN) based methods. Therefore, a number of highly accurate frequency domain methods to detect such GAN generated images have been proposed in the literature. Our study in this paper introduces a pipeline to mitigate the spectral artifacts. We show from our experiments that the artifacts in frequency spectrum of such fake images can be mitigated by proposed methods, which leads to the sharp decrease of performance of spectrum-based detectors. This paper also presents experimental results using a large database of images that are synthesized using BigGAN, CRN, CycleGAN, IMLE, Pro-GAN, StarGAN, StyleGAN and StyleGAN2 (including synthesized high resolution fingerprint images) to illustrate effectiveness of the proposed methods. Furthermore, we select a spatial-domain based fake image detector and observe a notable decrease in the detection performance when proposed method is incorporated. In summary, our insightful analysis and pipeline presented in this paper cautions the forensic community on the reliability of GAN-generated fake image detectors that are based on the analysis of frequency artifacts as these artifacts can be easily mitigated.

This paper presents an accelerated iterative Landweber method for nonlinear ultrasonic tomographic imaging in a multiple-input multiple-output (MIMO) configuration under a sparsity constraint on the image. The proposed method introduces the emerging MIMO signal processing techniques and target sparseness constraints in the traditional computational imaging field, thus significantly improves the speed of image reconstruction compared with the conventional imaging method while producing high quality images. Using numerical examples, we demonstrate that incorporating prior knowledge about the imaging field such as target sparseness accelerates significantly the convergence of the iterative imaging method, which provides considerable benefits to real-time tomographic imaging applications.

This paper develops a fast ultrasonic tomographic imaging method in a multiple-input multiple-output (MIMO) configuration using the propagation and backpropagation (PBP) method. By this method, ultrasonic excitation signals from multiple sources are transmitted simultaneously to probe the objects immersed in the medium. The scattering signals are recorded by multiple receivers. Utilizing the nonlinear ultrasonic wave propagation equation and the received time domain scattered signals, the objects are to be reconstructed iteratively in three steps. First, the propagation step calculates the predicted acoustic potential data at the receivers using an initial guess. Second, the difference signal between the predicted value and the measured data is calculated. Third, the backpropagation step computes updated acoustical potential data by backpropagating the difference signal to the same medium computationally. Unlike the conventional PBP method for tomographic imaging where each source takes turns to excite the acoustical field until all the sources are used, the developed MIMO-PBP method achieves faster image reconstruction by utilizing multiple source simultaneous excitation. Furthermore, we develop an orthogonal waveform signaling method using a waveform delay scheme to reduce the impact of speckle patterns in the reconstructed images. By numerical experiments we demonstrate that the proposed MIMO-PBP tomographic imaging method results in faster convergence and achieves superior imaging quality.

This paper presents a new time domain electromagnetic tomographic imaging algorithm using the propagation and back propagation method by solving a time dependent Maxwell's equation. The proposed algorithm reconstructs an extended object immersed in inhomogeneous medium using wide band electromagnetic pulse excitation. In this paper we develop an iterative yet convergent algorithm for reconstructing the dielectric profiles of the scattering medium for electromagnetic tomographic imaging purposes. The imaging algorithm employs the finite difference time domain forward propagation solver. The backpropagation solver iteratively reconstructs the object of interest. Using numerical examples, we demonstrate that the proposed algorithm results in high quality images with fast convergence.