DINO-MAC is a simple yet effective model for robust deepfake detection under degraded conditions and is the first-place winner of the “NTIRE 2026 - Robust Deepfake Detection Challenge @ CVPR 2026”, significantly outperforms all competitors.
An ensemble-based approach, namely the X-Iv2 Ensemble approach, merging Inception ResNet v2 and Xception Net based on their complementary architectures to enhance feature extraction and classification is introduced, integrating Explainable AI (XAI) using Integrated Gradients to interpret decision-making processes.
P. P. Sudharsana, R. R. Rajalaxmi· Intelligent Data Analysis· 0 citations
A robust, explainable detection framework is presented that combines a CNN backbone for extracting spatial artifacts with an LSTM module for modeling temporal inconsistencies across frames that enhances forensic decision support and increases practical readiness for content verification systems.
Lastone Banda, Esther J.· International Journal of Dat...· 0 citations
HiDD-Net is proposed, a hierarchical dual-domain distillation framework that combines a teacher network that integrates multi-granularity spatial views via a cross-attention mechanism to explicitly align fine-grained anomalies with global context, and a compact student model that achieves a competitive cross-dataset av...
Pro Proto-CBANet (Prototypical Class-Balanced Attention Network), which combines a 1D-CNN with an Average-Pooling Squeeze-and-Excitation block for feature recalibration, enabling distance-based reasoning in a latent metric space, and develops an advanced variant, HPN-GLF (Hyperspherical Prototypical Network with Global...
Chenxu Zhao· International Conference on...· 0 citations
This paper proposes a Deepfake tampering detection method based on a multimodal large model, termed LLM-FNP, which integrates frequency-domain noise perception with visual-language understanding and outperforming classical methods such as ResNet+LSTM.
This paper identifies key open challenges and concludes by highlighting a research gap for probabilistically robust, lightweight frameworks, offering a roadmap for future research toward reliable, real-time deepfake forensics in unconstrained environments.
Azhar Abdulmughni, Nagi Al-shaibany· مجلة جامعة صنعاء للعلوم التط...· 0 citations
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