Deepfakes leverage artificial intelligence to generate highly realistic but falsified visual content, raising concerns for security and trust in digital media. Detecting such manipulations becomes more challenging when videos are compressed, as compression algorithms introduce artifacts that obscure forensic evidence. One possible solution is to train separate models for different compression levels; however, this approach increases computational costs and limits scalability. To address this challenge, we introduce a unified framework designed to improve robustness against varying degrees of video compression. Our approach combines (i) a dedicated MPEG-based augmentation strategy tailored for compressed videos, and (ii) two architectural designs named Multi-Head (MHN) and the Multi-Branch Network (MBN). The MHN extends a standard backbone by appending lightweight output layers, or ”heads”, that jointly predict deepfake likelihood and compression level, enabling compression-aware detection with minimal architectural changes. The MBN combines multiple MHNs into a modular, parallel architecture, offering an alternative to conventional depth-based model scaling. Experiments on the FaceForensics++ and Celeb-DF datasets show that both MHN and MBN improve detection performance in compressed scenarios. Notably, MHN applied to a lightweight backbone outperforms deeper and more complex models without the multi-head extension, making the proposed solution well-suited for deployment in resource-constrained settings.

Detecting deepfake videos remains a challenging task, especially in scenarios involving unknown manipulation methods or unseen data distributions. Most existing video deepfake detection methods rely on high-level semantic features, which often lead to overfitting of facial identity information and poor transferability. In this work, we explore a novel perspective by modeling videos through 3D differential operations along temporal and spatial dimensions. To exploit the spatial–temporal variation information of the video content, the proposed approach decomposes videos into single-axis 1D differential signals, which are then transformed into 2D representations for efficient learning. This procedure enables the use of lightweight 2D CNNs while retaining directional forgery cues. Our experiments, aimed at analyzing whether these differential signals capture discriminative patterns useful for distinguishing real from fake content, show that the proposed method achieves strong intra-dataset performance and reveals complementary information across dimensions. These findings suggest that differential signals could potentially support generalization when integrated into broader detection frameworks. • We propose 3D Differential Decomposition modeling for deepfake video detection. • Multi-directional and multi-order differential operation are considered. • Optimization for differential order selection and fusion strategy are explored.

ICAO-compliant facial images, initially designed for secure biometric passports, are increasingly becoming central to identity verification in a wide range of application contexts, including border control, digital travel credentials, and financial services. While their standardization enables global interoperability, it also facilitates practices such as morphing and deepfakes, which can be exploited for harmful purposes like identity theft and illegal sharing of identity documents. Traditional countermeasures like Presentation Attack Detection (PAD) are limited to real-time capture and offer no post-capture protection. This survey paper investigates digital watermarking and steganography as complementary solutions that embed tamper-evident signals directly into the image, enabling persistent verification without compromising ICAO compliance. We provide the first comprehensive analysis of state-of-the-art techniques to evaluate the potential and drawbacks of the underlying approaches concerning the applications involving ICAO-compliant images and their suitability under standard constraints. We highlight key trade-offs, offering guidance for secure deployment in real-world identity systems.

The Fingerprint Liveness Detection Competition (LivDet) is a recurring benchmark series that evaluates the effectiveness of software-based Presentation Attack Detection (PAD) algorithms in fingerprint recognition. LivDet2025 presents three challenges: (1) "Liveness Detection in Action", requiring the integration of PAD with user-specific recognition; (2) "Fingerprint Representation", evaluating the compactness and discriminability of feature vectors; and (3) "Adversarial Robustness", assessing the resilience of PADs to adversarially-crafted presentation attack instruments. This edition marks a significant milestone with the inclusion of contactless fingerprint data, promoting interoperability and robustness across acquisition technologies. Furthermore, no training data was provided; participants must select and declare external datasets for model development. The competition was open to academic and industrial research groups, with all submitted algorithms evaluated on common datasets and under standardized protocols. LivDet2025 aims to provide a comprehensive assessment of PAD performance under realistic, multi-sensor, and multi-attack scenarios. Results reveal important trade-offs between PAD accuracy, usability, and computational efficiency. For instance, some systems achieved high presentation attack rejection at the cost of extremely high false rejection rates, while others optimised speed and generalizability but exhibited limited attack resilience.

Modern identity verification systems increasingly rely on facial images embedded in biometric documents such as electronic passports. To ensure global interoperability and security, these images must comply with strict standards defined by the International Civil Aviation Organization (ICAO), which specify acquisition, quality, and format requirements. However, once issued, these images may undergo unintentional degradations (e.g., compression, resizing) or malicious manipulations (e.g., morphing) and deceive facial recognition systems. In this study, we explore fragile watermarking, based on deep steganographic embedding as a proactive mechanism to certify the authenticity of ICAO-compliant facial images. By embedding a hidden image within the official photo at the time of issuance, we establish an integrity marker that becomes sensitive to any post-issuance modification. We assess how a range of image manipulations affects the recovered hidden image and show that degradation artifacts can serve as robust forensic cues. Furthermore, we propose a classification framework that analyzes the revealed content to detect and categorize the type of manipulation applied. Our experiments demonstrate high detection accuracy, including cross-method scenarios with multiple deep steganography-based models. These findings support the viability of fragile watermarking via steganographic embedding as a valuable tool for biometric document integrity verification.

Abstract Nowadays, fingerprint Presentation Attack Detection systems (PADs) are primarily based on deep learning architectures subjected to massive training. However, their performance decreases to never-seen-before attacks. With the goal of contributing to explaining this issue, we hypothesized that this limited ability to generalize is due to the lack of "representativeness" of the samples available for the PAD training. "Representativeness" is treated here from a geometrical perspective: the spread of samples into the feature space, especially near the decision boundaries. In particular, we explored the possibility of adopting three-dimensionality reduction methods to make the problem affordable through visual inspection. These methods enable visual inspection and interpretation by projecting data into two-dimensional spaces, facilitating the identification of weak areas in the decision regions estimated after the training phase. Our analysis delineates the benefits and drawbacks of each dimensionality reduction method and leads us to make substantial recommendations in the crucial phase of the training design.

The harmful utilization of DeepFake technology poses a significant threat to public welfare, precipitating a crisis in public opinion. Existing detection methodologies, predominantly relying on convolutional neural networks and deep learning paradigms, focus on achieving high in-domain recognition accuracy amidst many forgery techniques. However, overseeing the intricate interplay between textures and artifacts results in compromised performance across diverse forgery scenarios. This paper introduces a groundbreaking framework, denoted as Texture and Artifact Detector (TAD), to mitigate the challenge posed by the limited generalization ability stemming from the mutual neglect of textures and artifacts. Specifically, our approach delves into the similarities among disparate forged datasets, discerning synthetic content based on the consistency of textures and the presence of artifacts. Furthermore, we use a model ensemble learning strategy to judiciously aggregate texture disparities and artifact patterns inherent in various forgery types, thereby enabling the model’s generalization ability. Our comprehensive experimental analysis, encompassing extensive intra-dataset and cross-dataset validations along with evaluations on both video sequences and individual frames, confirms the effectiveness of TAD. The results from four benchmark datasets highlight the significant impact of the synergistic consideration of texture and artifact information, leading to a marked improvement in detection capabilities.

The International Fingerprint Liveness Detection Competition (LivDet) is a biennial event that invites academic and industry participants to prove their advancements in Fingerprint Presentation Attack Detection (PAD). This edition, LivDet2023, proposed two challenges, “Liveness Detection in Action” and “Fingerprint Representation”, to evaluate the efficacy of PAD embedded in verification systems and the effectiveness and compactness of feature sets. A third, “hidden” challenge is the inclusion of two subsets in the training set whose sensor information is unknown, testing participants’ ability to generalize their models. Only bona fide fingerprint samples were provided to participants, and the competition reports and assesses the performance of their algorithms suffering from this limitation in data availability.

The International Fingerprint Liveness Detection Competition is an international biennial competition open to academia and industry with the aim to assess and report advances in Fingerprint Presentation Attack Detection. The proposed "Liveness Detection in Action" and "Fingerprint representation" challenges were aimed to evaluate the impact of a PAD embedded into a verification system, and the effectiveness and compactness of feature sets for mobile applications. Furthermore, we experimented a new spoof fabrication method that has particularly affected the final results. Twenty-three algorithms were submitted to the competition, the maximum number ever achieved by LivDet.

The diffusion of fingerprint verification systems for security applications makes it urgent to investigate the embedding of software-based presentation attack detection algorithms (PAD) into such systems. Companies and institutions need to know whether such integration would make the system more “secure” and whether the technology available is ready, and, if so, at what operational working conditions. Despite significant improvements, especially by adopting deep learning approaches to fingerprint PAD, current research did not state much about their effectiveness when embedded in fingerprint verification systems. We believe that the lack of works is explained by the lack of instruments to investigate the problem, that is, modeling the cause-effect relationships when two non-zero error-free systems work together. Accordingly, this paper explores the fusion of PAD into verification systems by proposing a novel investigation instrument: a performance simulator based on the probabilistic modeling of the relationships among the Receiver Operating Characteristics (ROC) of the two individual systems when PAD and verification stages are implemented sequentially. As a matter of fact, this is the most straightforward, flexible, and widespread approach. We carry out simulations on the PAD algorithms’ ROCs submitted to the most recent editions of LivDet (2017-2019), the state-of-the-art NIST Bozorth3, and the top-level Veryfinger 12 matchers. Reported experiments explore significant scenarios to get the conditions under which fingerprint matching with embedded PAD can improve, rather than degrade, the overall personal verification performance.

In this study we investigate a textural processing method of electroencephalography (EEG) signal as an indicator to estimate the driver's vigilance in a hypothetical Brain-Computer Interface (BCI) system. The novelty of the solution proposed relies on employing the one-dimensional Local Binary Pattern (1D-LBP) algorithm for feature extraction from pre-processed EEG data. From the resulting feature vector, the classification is done according to three vigilance classes: awake, tired and drowsy. The claim is that the class transitions can be detected by describing the variations of the micro-patterns' occurrences along the EEG signal. The 1D-LBP is able to describe them by detecting mutual variations of the signal temporarily “close” as a short bit-code. Our analysis allows to conclude that the 1D-LBP adoption has led to significant performance improvement. Moreover, capturing the class transitions from the EEG signal is effective, although the overall performance is not yet good enough to develop a BCI for assessing the driver's vigilance in real environments.

In the last five years, deep learning methods, in particular CNN, have attracted considerable attention in the field of face-based recognition, achieving impressive results. Despite this progress, it is not yet clear precisely to what extent deep features are able to follow all the intra-class variations that the face can present over time. In this paper we investigate the performance the performance improvement of face recognition systems by adopting self updating strategies of the face templates. For that purpose, we evaluate the performance of a well-known deep-learning face representation, namely, FaceNet, on a dataset that we generated explicitly conceived to embed intra-class variations of users on a large time span of captures: the APhotoEveryday (APE) dataset11https://github.com/PRALabBiometrics/APhotoEverydayDB. Moreover, we compare these deep features with handcrafted features extracted using the BSIF algorithm. In both cases, we evaluate various template update strategies, in order to detect the most useful for such kind of features. Experimental results show the effectiveness of “optimized” self-update methods with respect to systems without update or random selection of templates.