arXiv:2607. 21776v1 Announce Type: new Abstract: Talking-face (TF) deepfake generation synthesizes photore- alistic facial video from a static source image and an au- dio signal, producing forgeries that current image-based detectors consistently fail to identify.
This paper presents an interpretable deepfake detection framework that explicitly encodes physically grounded forensic cues to analyze spatially and temporally coherent facial features in video sequences. The method transforms videos into identity-consistent facial trajectories, segments them into fixed-length temporal windows, and represents each frame with 68 structured descriptors across photometric, textural, geometric, and compression domains. These descriptors are processed by an LSTM to capture temporal dependencies, achieving strong F1-scores on four benchmark datasets and demonstrating robust cross-dataset generalization.
arXiv:2609.14437v1 Announce Type: cross Abstract: Deepfake detection systems often exhibit significant performance degradation when deployed on unseen manipulation methods, limiting their reliability...
The paper introduces Band-Attention Modulation Network (BAM‑Net), a face forgery detection framework that learns fine‑grained, adaptive modulation of frequency bands in the Discrete Cosine Transform spectrogram. BAM‑Net dynamically reweights anti‑diagonal frequency bands to enhance forgery‑related spectral cues while suppressing irrelevant information, then fuses this modulated frequency data with spatial features using a lightweight backbone with distance‑decayed attention. Experiments on FaceForensics++, Celeb‑DF, and DFDC show that BAM‑Net achieves state‑of‑the‑art performance and strong generalization across datasets, compression levels, and manipulation types.
arXiv:2609.01511v1 Announce Type: new Abstract: Face forgery detectors often achieve strong results on controlled benchmarks, but their reliability under realistic image degradations remains limited....
arXiv:2610.29442v1 Announce Type: new Abstract: Recent advances in generative models have enabled highly realistic face forgeries, yet generalizing deepfake detectors to unseen manipulation pipelines...
Spectra is a deepfake detection framework that uses implicit neural representations (INRs) to analyze how an image is reconstructed, producing multi-level reconstruction heatmaps. These heatmaps are fused with semantic visual features via a bidirectional mechanism, enabling the model to detect forgery cues that are less tied to specific generation pipelines. Experiments on several deepfake benchmarks show that Spectra outperforms detectors relying only on RGB appearance or semantic representations, and it also extends to face presentation attack detection.
arXiv:2608. 06865v1 Announce Type: cross Abstract: The malicious use of generative artificial intelligence to create highly realistic deepfake videos raises serious ethical concerns and poses substantial challenges to AI safety.
The paper introduces FADNet, a face authenticity detector that treats forgery detection as a one‑class classification problem, training only on authentic faces. It uses Evidential Deep Learning to measure uncertainty and a pseudo‑forgery generator to sharpen decision boundaries. Experiments on DF40 and ASFD show FADNet surpasses state‑of‑the‑art methods with 96.63% accuracy and 98.83% precision.
arXiv:2605. 27944v2 Announce Type: replace Abstract: With rapid advances in audio-visual generative models, reliable forgery detection becomes increasingly critical.
arXiv:2609.26274v1 Announce Type: new Abstract: The rapid evolution of generative AI (e.g., Sora, Hunyuan) makes it essential to develop effective detection strategies that can generalize across ever...
arXiv:2608.23363v1 Announce Type: cross Abstract: Audio-visual deepfake detection is an actively studied topic, where one of the main challenges is to develop detectors able to generalize across deep...