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:2609.12668v1 Announce Type: new Abstract: Recent deepfake detection studies increasingly suggest remote photoplethysmography (rPPG) signals as an authenticity cue. However, existing benchmarks...
arXiv:2411. 19715v4 Announce Type: replace-cross Abstract: We describe Forensics Adapter, an adapter network designed to transform CLIP into an effective and generalizable face forgery detector.
arXiv:2607. 17441v1 Announce Type: cross Abstract: Deepfake generation has raised growing concerns regarding digital media authenticity, misinformation, identity fraud, and public trust.
The paper introduces a video forgery detection system that fuses spatial and frequency-domain features using a ResNet‑LSTM backbone with a Convolutional Block Attention Module (CBAM) and a Discrete Cosine Transform (DCT) module. Experiments on multiple benchmark datasets show that this hybrid architecture outperforms existing methods in distinguishing authentic from manipulated videos. Ablation and comparative studies confirm the individual contributions of each component, highlighting the model’s effectiveness across diverse forgery scenarios.
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 Separable Prompt Learning (SePL), a method that enhances face forgery detection by leveraging CLIP’s textual encoder through two separate learnable prompts. SePL incorporates a cross-modality alignment strategy and specific objectives to distill forgery knowledge from CLIP. Experiments show that SePL outperforms existing approaches in cross-dataset and cross-method evaluations.
The paper introduces IMFD, an end‑to‑end multi‑face forgery detector that uses instruction‑based Large Vision‑Language Models (LVLMs). IMFD jointly localizes faces and predicts forgery labels in a single stage, explicitly incorporating predicted face bounding boxes into the textual instruction to improve grounding and detection. Experiments on converted multi‑face forgery datasets show that IMFD outperforms several state‑of‑the‑art methods.
arXiv:2608.30714v1 Announce Type: new Abstract: Verifying image authenticity is increasingly difficult, posing serious risks across journalism, law enforcement, and political domains. Most existing f...
arXiv:2610.01544v1 Announce Type: new Abstract: Existing image forgery detectors often suffer from generalization to unseen manipulation methods due to the limited ability to capture transferable for...
Verifying image authenticity is increasingly difficult, posing serious risks across journalism, law enforcement, and political domains. Most existing forensic methods rely on high-level visual artifac...
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.