Artem Filippov

For the Explainable Deepfake Detection Challenge on XPlainVerse, the authors present a modular detection-and-explanation system in which a detector fuses several DINOv3 backbones with Mesorch forensic features, a Grounding-DINO pipeline converts local artifact descriptions from explanations into weak patch-level pseudo-masks supervising an Artifact Evidence Map, and a local patch-level contrastive objective separates artifact from authenticity evidence, while class-conditional Qwen3-VL models generate complex explanations and a GRPO-optimized model simplifies them, reaching 0.9349 detection accuracy, a 0.5571 explanation score, and a 0.7456 final challenge score on the full test split.

For the Explainable Deepfake Detection Challenge on XPlainVerse, the authors present a modular detection-and-explanation system in which a detector fuses several DINOv3 backbones with Mesorch forensic features, a Grounding-DINO pipeline converts local artifact descriptions from explanations into weak patch-level pseudo-masks supervising an Artifact Evidence Map, and a local patch-level contrastive objective separates artifact from authenticity evidence, while class-conditional Qwen3-VL models generate complex explanations and a GRPO-optimized model simplifies them, reaching 0.9349 detection accuracy, a 0.5571 explanation score, and a 0.7456 final challenge score on the full test split.