arXiv AI
Sep 25

Band-Attention Modulation Network for Robust Face Forgery Detection

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.

By Zhida Zhang, Wenkui Yang, Xinlei Ma, Qihang Fan, Jie Cao
arXiv Computer Vision
Sep 24

Spatiality-Frequency Domain Video Forgery Detection System Based on ResNet-LSTM-CBAM and DCT Hybrid Network

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.

By Zihao Liao, Sheng Hong, Yu Chen
arXiv Computer Vision
Sep 3

From Detection to Localization: A Unified Forensics Framework for Fully Synthetic and Tampered Images

The paper proposes a unified forensics framework that extends traditional binary image manipulation detection to a multiclass setting—distinguishing real, fully synthetic, and tampered images. It adds a segmentation branch for pixel‑level localization of tampered regions, achieving higher classification accuracy and IoU scores compared to recent benchmarks. The authors provide the implementation on GitHub for reproducibility.

By Annalisa Gallina, Marco Fiorucci, Marco Brigo, Federica Battisti, Lamberto Ballan