Veritas: Generalizable Deepfake Detection via Pattern-Aware Reasoning

Core task: generalizable deepfake detection via pattern-aware reasoning. The field has evolved into a rich landscape of complementary strategies, each addressing different facets of the generalization challenge. At the highest level, the taxonomy reveals several major branches: frequency and spectral domain analysis (e.g., Frequency-aware Detection[1], Synthetic Frequency Patterns[3]) exploits artifacts in the frequency spectrum that persist across generation methods; temporal and spatiotemporal reasoning captures inconsistencies over time; feature disentanglement and decomposition (e.g., Texture Artifact Decomposition[8]) separates content from manipulation traces; large pre-trained model adaptation leverages foundation models such as CLIP or large language models; data augmentation and synthetic training strategies create diverse training signals; domain adaptation and generalization strategies (e.g., Invariant Risk Minimization[25]) explicitly optimize for cross-domain robustness; meta-learning and few-shot detection (e.g., Meta-Learning Relation Embedding[22]) enable rapid adaptation to novel forgeries; local and patch-level analysis (e.g., Patch-Discontinuity Mining[34]) focuses on fine-grained spatial cues; identity and semantic consistency analysis checks for logical coherence; noise pattern and forensic trace analysis mines low-level statistical signatures; attention mechanisms and architectural innovations introduce novel inductive biases; baseline and specialized architectures provide reference points; and surveys (e.g., Robust Detection Survey[6]) synthesize the state of the art. A particularly active line of work centers on adapting large pre-trained models to deepfake detection, where methods such as C2P-CLIP[5] and DeepFake-Adapter[23] fine-tune vision-language or vision-only backbones to capture generalizable forgery patterns. Within this branch, a small but growing cluster explores multimodal large language model reasoning, combining visual and textual modalities to perform more interpretable, context-aware detection. Veritas[0] sits squarely in this cluster, alongside Skyra[15] and EDVD-LLaMA[32], all of which harness the reasoning capabilities of large language models to identify subtle inconsistencies that simpler architectures might miss. Compared to Skyra[15], which emphasizes cross-modal alignment, and EDVD-LLaMA[32], which integrates video-level temporal cues, Veritas[0] focuses on pattern-aware reasoning that bridges low-level forensic traces with high-level semantic understanding. This direction reflects a broader trend toward interpretable, reasoning-driven detection, contrasting with purely data-driven approaches in frequency analysis or meta-learning branches, and highlights ongoing questions about how best to combine domain-specific inductive biases with the flexibility of foundation models.