VLAForge (CVPR 2026)

Official PyTorch implementation of "Unleashing Vision-Language Semantics for Deepfake Video Detection".

Overview

Recent Deepfake Video Detection (DFD) studies have demonstrated that pre-trained Vision-Language Models (VLMs) such as CLIP exhibit strong generalization capabilities in detecting artifacts across different identities. However, existing approaches focus on leveraging visual features only, overlooking their most distinctive strength — the rich vision-language semantics embedded in the latent space. We propose VLAForge, a novel DFD framework that unleashes the potential of such cross-modal semantics to enhance model's discriminability in deepfake detection. This work i) enhances the visual perception of VLM through a ForgePerceiver, which acts as an independent learner to capture diverse, subtle forgery cues both granularly and holistically, while preserving the pretrained Vision–Language Alignment (VLA) knowledge, and ii) provides a complementary discriminative cue — Identity-Aware VLA score, derived by coupling cross-modal semantics with the forgery cues learned by ForgePerceiver. Notably, the VLA score is augmented by an identity prior-informed text prompting to capture authenticity cues tailored to each identity, thereby enabling more discriminative cross-modal semantics. Comprehensive experiments on video DFD benchmarks, including classical face-swapping forgeries and recent full-face generation forgeries, demonstrate that our VLAForge substantially outperforms state-of-the-art methods at both frame and video levels.

Setup

Run

conda env create -f environment.yml

to create the virtual enviroment.

Device

• Single NVIDIA GeForce RTX 3090

Prepare Your Data

Step 1. Download the Deepfake Detection Datasets

• FaceForensics++, CDF-v1, CDF-v2, Deepfake Detection Challenge, DeepfakeDetection

• VQGAN, SiT-XL/2, DiT, PixArt are from DF40 (Celeb-DF).

Step 2. The JSON files are provided in JSONs.

Step 3. Download the Pre-train Models on [Google Drive].

Run VLAForge

Quick Start

• Set test_dataset to the name of the test dataset in test.ymal. Then, run

bash test.sh

Training

• Set train_dataset to the name of the test dataset in train.ymal. Then, train your own weights by runing

bash train.sh

Citation

• If you find the implementation useful, we would appreciate your acknowledgement via citing our VLAForge paper:

@inproceedings{zhu2026dfd,
  title={Unleashing Vision-Language Semantics for Deepfake Video Detection},
  author={Jiawen Zhu, Yunqi Miao, Xueyi Zhang, Jiankang Deng, Guansong Pang},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2026}
}