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.

Large-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. With the recent advances in generative models, recently several works proposed generating synthetic face recognition datasets to mitigate concerns in web-crawled face recognition datasets. This paper presents the summary of the Synthetic Data for Face Recognition (SDFR) Competition held in conjunction with the 18th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2024) and established to investigate the use of synthetic data for training face recognition models. The SDFR competition was split into two tasks, allowing participants to train face recognition systems using new synthetic datasets and/or existing ones. In the first task, the face recognition backbone was fixed and the dataset size was limited, while the second task provided almost complete freedom on the model backbone, the dataset, and the training pipeline. The submitted models were trained on existing and also new synthetic datasets and used clever methods to improve training with synthetic data. The submissions were evaluated and ranked on a diverse set of seven benchmarking datasets. The paper gives an overview of the submitted face recognition models and reports achieved performance compared to baseline models trained on real and synthetic datasets. Furthermore, the evaluation of submissions is extended to bias assessment across different demography groups. Lastly, an outlook on the current state of the research in training face recognition models using synthetic data is presented, and existing problems as well as potential future directions are also discussed.

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.

When deepfakes are widespread on chatting platforms, they are expected to be subject to heavy resizing and compressing steps. In this paper, we present a tensor-based representation of compressed and resized images. Tensor embeds DCT features computed on multi-scaled and multi-compressed versions of the input facial image. Moreover, a custom deep-architecture is designed and trained on the proposed representation. Experimental results show its pros and cons with respect to state-of-the-art methods.

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.