Seeing What Matters: Generalizable AI-generated Video Detection with Forensic-Oriented Augmentation

• URL: http://arxiv.org/abs/2506.16802v1
• Date: Fri, 20 Jun 2025 07:36:59 GMT
• Title: Seeing What Matters: Generalizable AI-generated Video Detection with Forensic-Oriented Augmentation
• Authors: Riccardo Corvi, Davide Cozzolino, Ekta Prashnani, Shalini De Mello, Koki Nagano, Luisa Verdoliva,
• Abstract summary: Synthetic video generation can produce very realistic high-resolution videos that are virtually indistinguishable from real ones.<n>Several video forensic detectors have been recently proposed, but they often exhibit poor generalization.<n>We introduce a novel data augmentation strategy based on the wavelet decomposition and replace specific frequency-related bands to drive the model to exploit more relevant forensic cues.<n>Our method achieves a significant accuracy improvement over state-of-the-art detectors and obtains excellent results even on very recent generative models.
• Score: 18.402668470092294
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Synthetic video generation is progressing very rapidly. The latest models can produce very realistic high-resolution videos that are virtually indistinguishable from real ones. Although several video forensic detectors have been recently proposed, they often exhibit poor generalization, which limits their applicability in a real-world scenario. Our key insight to overcome this issue is to guide the detector towards seeing what really matters. In fact, a well-designed forensic classifier should focus on identifying intrinsic low-level artifacts introduced by a generative architecture rather than relying on high-level semantic flaws that characterize a specific model. In this work, first, we study different generative architectures, searching and identifying discriminative features that are unbiased, robust to impairments, and shared across models. Then, we introduce a novel forensic-oriented data augmentation strategy based on the wavelet decomposition and replace specific frequency-related bands to drive the model to exploit more relevant forensic cues. Our novel training paradigm improves the generalizability of AI-generated video detectors, without the need for complex algorithms and large datasets that include multiple synthetic generators. To evaluate our approach, we train the detector using data from a single generative model and test it against videos produced by a wide range of other models. Despite its simplicity, our method achieves a significant accuracy improvement over state-of-the-art detectors and obtains excellent results even on very recent generative models, such as NOVA and FLUX. Code and data will be made publicly available.

Related papers

• BrokenVideos: A Benchmark Dataset for Fine-Grained Artifact Localization in AI-Generated Videos [63.03271511550633]
  BrokenVideos is a benchmark dataset of 3,254 AI-generated videos with meticulously annotated, pixel-level masks highlighting regions of visual corruption.<n>Our experiments show that training state of the art artifact detection models and multi modal large language models (MLLMs) on BrokenVideos significantly improves their ability to localize corrupted regions.
  arXiv  Detail & Related papers  (2025-06-25T03:30:04Z)
• DAVID-XR1: Detecting AI-Generated Videos with Explainable Reasoning [58.70446237944036]
  DAVID-X is the first dataset to pair AI-generated videos with detailed defect-level, temporal-spatial annotations and written rationales.<n>We present DAVID-XR1, a video-language model designed to deliver an interpretable chain of visual reasoning.<n>Our results highlight the promise of explainable detection methods for trustworthy identification of AI-generated video content.
  arXiv  Detail & Related papers  (2025-06-13T13:39:53Z)
• TalkingHeadBench: A Multi-Modal Benchmark & Analysis of Talking-Head DeepFake Detection [8.166056418369125]
  TalkingHeadBench is a benchmark for deepfake talking-head detection.<n>Our dataset includes deepfakes synthesized by leading academic and commercial models.<n>We provide error analysis using Grad-CAM visualizations to expose common failure modes and detectorizable biases.
  arXiv  Detail & Related papers  (2025-05-30T17:59:08Z)
• AI-GenBench: A New Ongoing Benchmark for AI-Generated Image Detection [9.540547388707987]
  Ai-GenBench is a novel benchmark designed to address the need for robust detection of AI-generated images in real-world scenarios.<n>By establishing clear evaluation rules and controlled augmentation strategies, Ai-GenBench enables meaningful comparison of detection methods and scalable solutions.
  arXiv  Detail & Related papers  (2025-04-29T15:41:13Z)
• Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization [6.589206192038365]
  This paper proposes an adversarial meta-learning algorithm using task-specific adaptive sample synthesis and consistency regularization. It boosts both robustness and generalization of the model. Experimental results demonstrate the model's consistent performance across various datasets, outperforming the models in comparison.
  arXiv  Detail & Related papers  (2024-11-12T19:55:07Z)
• 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)
• MissionGNN: Hierarchical Multimodal GNN-based Weakly Supervised Video Anomaly Recognition with Mission-Specific Knowledge Graph Generation [5.0923114224599555]
  Video Anomaly Detection and Video Anomaly Recognition are critically important for applications in intelligent surveillance, evidence investigation, violence alerting, etc.<n>These tasks face significant challenges due to the rarity of anomalies which leads to extremely imbalanced data and the impracticality of extensive frame-level data annotation for supervised learning.<n>This paper introduces a novel hierarchical graph neural network (GNN) based model MissionGNN that addresses these challenges by leveraging a state-of-the-art large language model and a comprehensive knowledge graph for efficient weakly supervised learning in VAR.
  arXiv  Detail & Related papers  (2024-06-27T01:09:07Z)
• AI-Generated Video Detection via Spatio-Temporal Anomaly Learning [2.1210527985139227]
  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.
  arXiv  Detail & Related papers  (2024-03-25T11:26:18Z)
• GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning [50.7702397913573]
  The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology. We propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection.
  arXiv  Detail & Related papers  (2024-02-03T03:13:50Z)
• Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis [69.09526348527203]
  Deep generative models have led to highly realistic media, known as deepfakes, that are commonly indistinguishable from real to human eyes. We propose a novel fake detection that is designed to re-synthesize testing images and extract visual cues for detection. We demonstrate the improved effectiveness, cross-GAN generalization, and robustness against perturbations of our approach in a variety of detection scenarios.
  arXiv  Detail & Related papers  (2021-05-29T21:22:24Z)
• Firearm Detection via Convolutional Neural Networks: Comparing a Semantic Segmentation Model Against End-to-End Solutions [68.8204255655161]
  Threat detection of weapons and aggressive behavior from live video can be used for rapid detection and prevention of potentially deadly incidents. One way for achieving this is through the use of artificial intelligence and, in particular, machine learning for image analysis. We compare a traditional monolithic end-to-end deep learning model and a previously proposed model based on an ensemble of simpler neural networks detecting fire-weapons via semantic segmentation.
  arXiv  Detail & Related papers  (2020-12-17T15:19:29Z)

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.