Spotting tell-tale visual artifacts in face swapping videos: strengths and pitfalls of CNN detectors

• URL: http://arxiv.org/abs/2506.16497v1
• Date: Thu, 19 Jun 2025 17:51:11 GMT
• Title: Spotting tell-tale visual artifacts in face swapping videos: strengths and pitfalls of CNN detectors
• Authors: Riccardo Ziglio, Cecilia Pasquini, Silvio Ranise,
• Abstract summary: Face swapping manipulations in video streams represents an increasing threat in remote video communications.<n>Recent literature proposes to characterize and exploit visual artifacts introduced in video frames by swapping algorithms.<n>This paper investigates the effectiveness of this approach by benchmarking CNN-based data-driven models on two data corpora.
• Score: 2.89209645531276
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Face swapping manipulations in video streams represents an increasing threat in remote video communications, due to advances in automated and real-time tools. Recent literature proposes to characterize and exploit visual artifacts introduced in video frames by swapping algorithms when dealing with challenging physical scenes, such as face occlusions. This paper investigates the effectiveness of this approach by benchmarking CNN-based data-driven models on two data corpora (including a newly collected one) and analyzing generalization capabilities with respect to different acquisition sources and swapping algorithms. The results confirm excellent performance of general-purpose CNN architectures when operating within the same data source, but a significant difficulty in robustly characterizing occlusion-based visual cues across datasets. This highlights the need for specialized detection strategies to deal with such artifacts.

Related papers

• Understanding Long Videos via LLM-Powered Entity Relation Graphs [51.13422967711056]
  GraphVideoAgent is a framework that maps and monitors the evolving relationships between visual entities throughout the video sequence.<n>Our approach demonstrates remarkable effectiveness when tested against industry benchmarks.
  arXiv  Detail & Related papers  (2025-01-27T10:57:24Z)
• 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)
• Improving Video Deepfake Detection: A DCT-Based Approach with Patch-Level Analysis [0.0]
  The I-frames were extracted in order to provide faster computation and analysis than approaches described in the literature. To identify the discriminating regions within individual video frames, the entire frame, background, face, eyes, nose, mouth, and face frame were analyzed separately. Experimental results show that the eye and mouth regions are those most discriminative and able to determine the nature of the video under analysis.
  arXiv  Detail & Related papers  (2023-10-17T12:30:46Z)
• Mitigating Representation Bias in Action Recognition: Algorithms and Benchmarks [76.35271072704384]
  Deep learning models perform poorly when applied to videos with rare scenes or objects. We tackle this problem from two different angles: algorithm and dataset. We show that the debiased representation can generalize better when transferred to other datasets and tasks.
  arXiv  Detail & Related papers  (2022-09-20T00:30:35Z)
• Multimodal Graph Learning for Deepfake Detection [10.077496841634135]
  Existing deepfake detectors face several challenges in achieving robustness and generalization. We propose a novel framework, namely Multimodal Graph Learning (MGL), that leverages information from multiple modalities. Our proposed method aims to effectively identify and utilize distinguishing features for deepfake detection.
  arXiv  Detail & Related papers  (2022-09-12T17:17:49Z)
• Video Salient Object Detection via Contrastive Features and Attention Modules [106.33219760012048]
  We propose a network with attention modules to learn contrastive features for video salient object detection. A co-attention formulation is utilized to combine the low-level and high-level features. We show that the proposed method requires less computation, and performs favorably against the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-11-03T17:40:32Z)
• HighlightMe: Detecting Highlights from Human-Centric Videos [52.84233165201391]
  We present a domain- and user-preference-agnostic approach to detect highlightable excerpts from human-centric videos. We use an autoencoder network equipped with spatial-temporal graph convolutions to detect human activities and interactions. We observe a 4-12% improvement in the mean average precision of matching the human-annotated highlights over state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-10-05T01:18:15Z)
• Finding Facial Forgery Artifacts with Parts-Based Detectors [73.08584805913813]
  We design a series of forgery detection systems that each focus on one individual part of the face. We use these detectors to perform detailed empirical analysis on the FaceForensics++, Celeb-DF, and Facebook Deepfake Detection Challenge datasets.
  arXiv  Detail & Related papers  (2021-09-21T16:18:45Z)
• Cloud based Scalable Object Recognition from Video Streams using Orientation Fusion and Convolutional Neural Networks [11.44782606621054]
  Convolutional neural networks (CNNs) have been widely used to perform intelligent visual object recognition. CNNs still suffer from severe accuracy degradation, particularly on illumination-variant datasets. We propose a new CNN method based on orientation fusion for visual object recognition.
  arXiv  Detail & Related papers  (2021-06-19T07:15:15Z)
• Coherent Loss: A Generic Framework for Stable Video Segmentation [103.78087255807482]
  We investigate how a jittering artifact degrades the visual quality of video segmentation results. We propose a Coherent Loss with a generic framework to enhance the performance of a neural network against jittering artifacts.
  arXiv  Detail & Related papers  (2020-10-25T10:48:28Z)
• Dynamic texture analysis for detecting fake faces in video sequences [6.1356022122903235]
  This work explores the analysis of texture-temporal dynamics of the video signal. The goal is to characterizing and distinguishing real fake sequences. We propose to build multiple binary decision on the joint analysis of temporal segments.
  arXiv  Detail & Related papers  (2020-07-30T07:21:24Z)
• Deepfakes Detection with Automatic Face Weighting [21.723416806728668]
  We introduce a method based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) that extracts visual and temporal features from faces present in videos to accurately detect manipulations. The method is evaluated with the Deepfake Detection Challenge dataset, providing competitive results compared to other techniques.
  arXiv  Detail & Related papers  (2020-04-25T00:47:42Z)

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.