Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuning

• URL: http://arxiv.org/abs/2408.17065v1
• Date: Fri, 30 Aug 2024 07:49:57 GMT
• Title: Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuning
• Authors: Zhiyuan Yan, Yandan Zhao, Shen Chen, Xinghe Fu, Taiping Yao, Shouhong Ding, Li Yuan,
• Abstract summary: Temporal features can be complex and diverse. Spatiotemporal models often lean heavily on one type of artifact and ignore the other. Videos are naturally resource-intensive.
• Score: 42.86270268974854
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Three key challenges hinder the development of current deepfake video detection: (1) Temporal features can be complex and diverse: how can we identify general temporal artifacts to enhance model generalization? (2) Spatiotemporal models often lean heavily on one type of artifact and ignore the other: how can we ensure balanced learning from both? (3) Videos are naturally resource-intensive: how can we tackle efficiency without compromising accuracy? This paper attempts to tackle the three challenges jointly. First, inspired by the notable generality of using image-level blending data for image forgery detection, we investigate whether and how video-level blending can be effective in video. We then perform a thorough analysis and identify a previously underexplored temporal forgery artifact: Facial Feature Drift (FFD), which commonly exists across different forgeries. To reproduce FFD, we then propose a novel Video-level Blending data (VB), where VB is implemented by blending the original image and its warped version frame-by-frame, serving as a hard negative sample to mine more general artifacts. Second, we carefully design a lightweight Spatiotemporal Adapter (StA) to equip a pretrained image model (both ViTs and CNNs) with the ability to capture both spatial and temporal features jointly and efficiently. StA is designed with two-stream 3D-Conv with varying kernel sizes, allowing it to process spatial and temporal features separately. Extensive experiments validate the effectiveness of the proposed methods; and show our approach can generalize well to previously unseen forgery videos, even the just-released (in 2024) SoTAs. We release our code and pretrained weights at \url{https://github.com/YZY-stack/StA4Deepfake}.

Related papers

• Learning Temporally Consistent Video Depth from Video Diffusion Priors [57.929828486615605]
  This work addresses the challenge of video depth estimation. We reformulate the prediction task into a conditional generation problem. This allows us to leverage the prior knowledge embedded in existing video generation models.
  arXiv  Detail & Related papers  (2024-06-03T16:20:24Z)
• Distinguish Any Fake Videos: Unleashing the Power of Large-scale Data and Motion Features [21.583246378475856]
  We introduce an extensive video dataset designed specifically for AI-Generated Video Detection (GenVidDet) We also present the Dual-Branch 3D Transformer (DuB3D), an innovative and effective method for distinguishing between real and generated videos. DuB3D can distinguish between real and generated video content with 96.77% accuracy, and strong generalization capability even for unseen types.
  arXiv  Detail & Related papers  (2024-05-24T08:26:04Z)
• Exploring Pre-trained Text-to-Video Diffusion Models for Referring Video Object Segmentation [72.90144343056227]
  We explore the visual representations produced from a pre-trained text-to-video (T2V) diffusion model for video understanding tasks. We introduce a novel framework, termed "VD-IT", tailored with dedicatedly designed components built upon a fixed T2V model. Our VD-IT achieves highly competitive results, surpassing many existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-03-18T17:59:58Z)
• AltFreezing for More General Video Face Forgery Detection [138.5732617371004]
  We propose to capture both spatial and unseen temporal artifacts in one model for face forgery detection. We present a novel training strategy called AltFreezing for more general face forgery detection.
  arXiv  Detail & Related papers  (2023-07-17T08:24:58Z)
• TAPE: Temporal Attention-based Probabilistic human pose and shape Estimation [7.22614468437919]
  Existing methods ignore the ambiguities of the reconstruction and provide a single deterministic estimate for the 3D pose. We present a Temporal Attention based Probabilistic human pose and shape Estimation method (TAPE) that operates on an RGB video. We show that TAPE outperforms state-of-the-art methods in standard benchmarks.
  arXiv  Detail & Related papers  (2023-04-29T06:08:43Z)
• HQ3DAvatar: High Quality Controllable 3D Head Avatar [65.70885416855782]
  This paper presents a novel approach to building highly photorealistic digital head avatars. Our method learns a canonical space via an implicit function parameterized by a neural network. At test time, our method is driven by a monocular RGB video.
  arXiv  Detail & Related papers  (2023-03-25T13:56:33Z)
• Deep Video Prior for Video Consistency and Propagation [58.250209011891904]
  We present a novel and general approach for blind video temporal consistency. Our method is only trained on a pair of original and processed videos directly instead of a large dataset. We show that temporal consistency can be achieved by training a convolutional neural network on a video with Deep Video Prior.
  arXiv  Detail & Related papers  (2022-01-27T16:38:52Z)
• Learnable Sampling 3D Convolution for Video Enhancement and Action Recognition [24.220358793070965]
  We introduce a new module to improve the capability of 3D convolution (emphLS3D-Conv) We add learnable 2D offsets to 3D convolution which aims to sample locations on spatial feature maps across frames. The experiments on video, video super-resolution, video denoising, and action recognition demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-11-22T09:20:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.