AI-Generated Video Detection via Spatio-Temporal Anomaly Learning

• URL: http://arxiv.org/abs/2403.16638v1
• Date: Mon, 25 Mar 2024 11:26:18 GMT
• Title: AI-Generated Video Detection via Spatio-Temporal Anomaly Learning
• Authors: Jianfa Bai, Man Lin, Gang Cao,
• Abstract summary: Users can easily create non-existent videos to spread false information. A large-scale generated video dataset (GVD) is constructed as a benchmark for model training and evaluation.
• Score: 2.1210527985139227
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The advancement of generation models has led to the emergence of highly realistic artificial intelligence (AI)-generated videos. Malicious users can easily create non-existent videos to spread false information. This letter proposes an effective AI-generated video detection (AIGVDet) scheme by capturing the forensic traces with a two-branch spatio-temporal convolutional neural network (CNN). Specifically, two ResNet sub-detectors are learned separately for identifying the anomalies in spatical and optical flow domains, respectively. Results of such sub-detectors are fused to further enhance the discrimination ability. A large-scale generated video dataset (GVD) is constructed as a benchmark for model training and evaluation. Extensive experimental results verify the high generalization and robustness of our AIGVDet scheme. Code and dataset will be available at https://github.com/multimediaFor/AIGVDet.

Related papers

• Zero-Shot Detection of AI-Generated Images [54.01282123570917]
  We propose a zero-shot entropy-based detector (ZED) to detect AI-generated images. Inspired by recent works on machine-generated text detection, our idea is to measure how surprising the image under analysis is compared to a model of real images. ZED achieves an average improvement of more than 3% over the SoTA in terms of accuracy.
  arXiv  Detail & Related papers  (2024-09-24T08:46:13Z)
• SIGMA:Sinkhorn-Guided Masked Video Modeling [69.31715194419091]
  Sinkhorn-guided Masked Video Modelling ( SIGMA) is a novel video pretraining method. We distribute features of space-time tubes evenly across a limited number of learnable clusters. Experimental results on ten datasets validate the effectiveness of SIGMA in learning more performant, temporally-aware, and robust video representations.
  arXiv  Detail & Related papers  (2024-07-22T08:04:09Z)
• Turns Out I'm Not Real: Towards Robust Detection of AI-Generated Videos [16.34393937800271]
  generative models in creating high-quality videos have raised concerns about digital integrity and privacy vulnerabilities. Recent works to combat Deepfakes videos have developed detectors that are highly accurate at identifying GAN-generated samples. We propose a novel framework for detecting videos synthesized from multiple state-of-the-art (SOTA) generative models.
  arXiv  Detail & Related papers  (2024-06-13T21:52:49Z)
• Exposing AI-generated Videos: A Benchmark Dataset and a Local-and-Global Temporal Defect Based Detection Method [31.763312726582217]
  generative model has made significant advancements in the creation of realistic videos, which causes security issues. In this paper, we first construct a video dataset using advanced diffusion-based video generation algorithms with various semantic contents. By analyzing local and global temporal defects of current AI-generated videos, a novel detection framework is constructed to expose fake videos.
  arXiv  Detail & Related papers  (2024-05-07T09:00:09Z)
• Video Anomaly Detection using GAN [0.0]
  This thesis study aims to offer the solution for this use case so that human resources won't be required to keep an eye out for any unusual activity in the surveillance system records. We have developed a novel generative adversarial network (GAN) based anomaly detection model.
  arXiv  Detail & Related papers  (2023-11-23T16:41:30Z)
• Denoising Diffusion Autoencoders are Unified Self-supervised Learners [58.194184241363175]
  This paper shows that the networks in diffusion models, namely denoising diffusion autoencoders (DDAE), are unified self-supervised learners. DDAE has already learned strongly linear-separable representations within its intermediate layers without auxiliary encoders. Our diffusion-based approach achieves 95.9% and 50.0% linear evaluation accuracies on CIFAR-10 and Tiny-ImageNet.
  arXiv  Detail & Related papers  (2023-03-17T04:20:47Z)
• Backdoor Attack Detection in Computer Vision by Applying Matrix Factorization on the Weights of Deep Networks [6.44397009982949]
  We introduce a novel method for backdoor detection that extracts features from pre-trained DNN's weights. In comparison to other detection techniques, this has a number of benefits, such as not requiring any training data. Our method outperforms the competing algorithms in terms of efficiency and is more accurate, helping to ensure the safe application of deep learning and AI.
  arXiv  Detail & Related papers  (2022-12-15T20:20:18Z)
• Neurosymbolic hybrid approach to driver collision warning [64.02492460600905]
  There are two main algorithmic approaches to autonomous driving systems. Deep learning alone has achieved state-of-the-art results in many areas. But sometimes it can be very difficult to debug if the deep learning model doesn't work.
  arXiv  Detail & Related papers  (2022-03-28T20:29:50Z)
• A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata [73.38551379469533]
  DAD:DeepAnomalyDetection is a new approach for automatic model learning and anomaly detection in hybrid production systems. It combines deep learning and timed automata for creating behavioral model from observations. The algorithm has been applied to few data sets including two from real systems and has shown promising results.
  arXiv  Detail & Related papers  (2020-10-29T08:27:43Z)
• Video Anomaly Detection Using Pre-Trained Deep Convolutional Neural Nets and Context Mining [2.0646127669654835]
  We show how to use pre-trained convolutional neural net models to perform feature extraction and context mining. We derive contextual properties from the high-level features to further improve the performance of our video anomaly detection method.
  arXiv  Detail & Related papers  (2020-10-06T00:26:14Z)
• Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing [65.78881372074983]
  IoT devices can hardly afford complex deep neural networks (DNN) models, and offloading anomaly detection tasks to the cloud incurs long delay. We propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems. We show that our proposed approach significantly reduces detection delay without sacrificing accuracy, as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-04-15T06:13:33Z)

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.