Zaznavanje obraznih ponaredkov s samonadzorovanim učenjem

In biometric identity verification, the growing fidelity of AI-synthesized faces is threatening the reliability of face recognition systems. Modern gen- erative models can create extremely realistic facial forgeries that often evade detection by current methods. Among these, morphing attacks are especially dangerous: by digitally merging the faces of two or more individuals, they yield a single image that can fool face recognition systems into matching multiple identities, thereby facilitating identity fraud and other malicious exploits. Most Morphing Attack Detection (MAD) approaches use super- vised learning on a fixed set of known morphing techniques. Such models often achieve high accuracy on morphs created by the same algorithms seen during training, but they tend to rely on method-specific artifacts and strug- gle to generalize to morphs from unseen techniques or under different data conditions. Unsupervised one-class methods avoid overfitting to specific at- tacks, but they often lack the sensitivity to detect the faint, distributed arti- facts left by high-quality morphs. To overcome these challenges, a new face forgery detection framework with improved robustness and generalization is introduced. The approach uses self-supervised training on synthetic forgery artifacts, which helps the detector learn decision boundaries that are more generic and resilient. At the heart of the model, a gating mechanism fuses two complementary information streams: the high-level semantic features from a vision-language foundation model and the fine-grained spatial features from a high-resolution convolutional network. The semantic branch is built on a CLIP vision-language backbone and fine-tuned via Low-Rank Adap- tation (LoRA) to adapt its image-text embeddings to the forgery detection task, enabling accurate discrimination between authentic and manipulated faces. In parallel, a high-resolution convolutional branch (based on HRNet) preserves detailed spatial information and aggregates multi-scale features, allowing it to capture even very subtle artifacts. An auxiliary segmentation module provides pixel-level guidance to this branch by distinguishing genuine facial regions from likely manipulated ones, which regularizes the training. By combining the CLIP branch’s global semantic context with the convolu- tional branch’s local artifact sensitivity, the model produces a well-balanced and highly discriminative representation for detecting face forgeries. The entire architecture is trained end-to-end with a composite loss that simulta- neously enforces semantic alignment, segmentation consistency, and classifi- cation accuracy. Evaluated on diverse morphing benchmarks, the proposed method achieves state-of-the-art performance. It significantly outperforms both supervised and unsupervised baseline detectors, attaining an average Equal Error Rate (EER) of just 0.85%. Notably, the improvements are most pronounced on high-quality morphs generated by advanced GAN and diffu- sion models, highlighting the framework’s resilience against next-generation forgery techniques.