Biometric verification requires that a biometric probe can be compared against a reference sample, which in the case of ICAO compliant MRTDs is stored as a JPEG or JPEG2000 image. In order to avoid equipping temporary ID documents with expensive RFID-chips for machine readability, the reference sample should be encoded in conventional 2D Data Matrix codes. This saves resources and speeds up the issuing process, but comes with the challenge of storing the face images at significantly smaller storage capacities. For this reason, it is important to reduce the file size of these images to a maximum of 1024 bytes. This study examines preprocessing steps and compression configurations that can be used to achieve this target size while minimizing the impact on the performance of face recognition algorithms. Therefore seven compression algorithms are examined, namely JPEG, JPEG 2000, JPEG XL, JPEG AI, HEIF, AVIF, and WebP. The results of this research show that AVIF is the most suitable compressor for images when using a resolution of $\mathbf{5 6 x 5 6}$ pixels and pre-smoothing outside the region of interest.
In this work, we take a step toward robust source identification for pushbroom satellite detectors by addressing source detector attribution in satellite imagery using Photo-Response Non-Uniformity (PRNU). As natural camera-based methods do not generalize well to satellite images, we adapt PRNU analysis to pushbroom sensors by extracting 1D reference signals per detector and comparing them with test segments. Experiments on Sentinel-2 data demonstrate that reliable fingerprints can be constructed from just a few images, making it possible to verify the authenticity of a L1B Sentinel-2 source.
Deepfake detection is increasingly critical for multimedia forensics, yet many detectors degrade under distribution shifts caused by unseen generation pipelines, post-processing, and unconstrained capture conditions. To improve cross-dataset generalization, we propose a powerfull two-stream detector, named HCSI-Net, that couples a CNN and a transformer with progressive interaction during hierarchical feature extraction. The streams are linked via a novel bi-directional spatial crossgating mechanism that jointly refines local texture cues and global contextual information across stages. The model is trained using manipulation-agnostic supervision based on simulated forgery artifacts and evaluated under challenging cross-dataset evaluation scenarios. Experiments across six widely used datasets demonstrate robust generalization across diverse deepfake generation techniques, achieving a macro-average AUC of 89.13 and consistently outperforming a number of strong state-of-theart baselines. Ablation results confirm that intermediate crossstream interaction drives the observed gains. Code is available at https://github.com/markobrodaric/HCSI-Net.
The generalization capabilities to cross and unknown scenarios are one of the main issues to solve in deepfake detection research. This work provides a new face-swap dataset generated by six algorithms, which can be used to enhance current classifiers. Our work focuses on tackling open challenges in current datasets, such as a small number of bona fide images, a limited number of subjects, and unevenly split datasets. Our new dataset consists of around $70 k$ high-resolution ($1024 \times 1024$) subject-disjoint face images, not lossy compressed, and faceswapped. This is essential to accurately classify current classifiers and improve the accuracy of future classifiers, as resolution of face-swaps will likely incline in the future. Additionally, this dataset is available for research purposes.11https://github.com/dasec/HDA-HQSwap.
Accurate finger position classification from fingerprint images plays a critical role in the efficiency of civil biometric identification systems. However, operational databases often contain missing or inconsistent position labels, which significantly degrade indexing performance. Most existing approaches address this task through independent classification of individual fingerprint images, ignoring the inherent global constraint that each finger position must appear exactly once in a valid tenprint set. This assumption leads to substantial error accumulation at the subject level. In this work, we propose FingerAssign, a structured prediction framework that integrates Convolutional Neural Network classifiers with linear assignment optimization to explicitly enforce the uniqueness constraint during inference. The method constructs cost matrices from the probabilistic outputs of the classifiers using probability, log probability, and logit transformations, and computes a globally consistent assignment across the ten fingerprints of each subject. We evaluate the proposed approach on the public SOCOFing and NIST SD14 datasets using multiple deep learning architectures. While independent classification achieves high per image accuracy, it produces poor subject level consistency, reaching only 13.3 % on SOCOFing and 63.3 % on NIST SD14. In contrast, FingerAssign significantly improves subject level performance to 76.7 % and 100 %, respectively, while also increasing per image accuracy. The results demonstrate that incorporating global constraints through combinatorial optimization effectively mitigates the limitations of isolated classification and provides a simple and robust solution for finger position identification in operational biometric systems.
