Video Forgery Detection for Surveillance Cameras: A Review
- URL: http://arxiv.org/abs/2505.03832v2
- Date: Mon, 28 Jul 2025 10:58:53 GMT
- Title: Video Forgery Detection for Surveillance Cameras: A Review
- Authors: Noor B. Tayfor, Tarik A. Rashid, Shko M. Qader, Bryar A. Hassan, Mohammed H. Abdalla, Jafar Majidpour, Aram M. Ahmed, Hussein M. Ali, Aso M. Aladdin, Abdulhady A. Abdullah, Ahmed S. Shamsaldin, Haval M. Sidqi, Abdulrahman Salih, Zaher M. Yaseen, Azad A. Ameen, Janmenjoy Nayak, Mahmood Yashar Hamza,
- Abstract summary: Surveillance footage plays a crucial role in security, law enforcement, and judicial processes.<n>With the rise of advanced video editing tools, tampering with digital recordings has become increasingly easy.<n> Ensuring the integrity of surveillance videos is essential, as manipulated footage can lead to misinformation and undermine judicial decisions.
- Score: 4.384167706308244
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The widespread availability of video recording through smartphones and digital devices has made video-based evidence more accessible than ever. Surveillance footage plays a crucial role in security, law enforcement, and judicial processes. However, with the rise of advanced video editing tools, tampering with digital recordings has become increasingly easy, raising concerns about their authenticity. Ensuring the integrity of surveillance videos is essential, as manipulated footage can lead to misinformation and undermine judicial decisions. This paper provides a comprehensive review of existing forensic techniques used to detect video forgery, focusing on their effectiveness in verifying the authenticity of surveillance recordings. Various methods, including compression-based analysis, frame duplication detection, and machine learning-based approaches, are explored. The findings highlight the growing necessity for more robust forensic techniques to counteract evolving forgery methods. Strengthening video forensic capabilities will ensure that surveillance recordings remain credible and admissible as legal evidence.
Related papers
- VITED: Video Temporal Evidence Distillation [49.38292490256531]
We investigate complex video question answering via chain-of-evidence reasoning.<n>Models struggle with multi-step reasoning as they uniformly sample a fixed number of frames.<n>We propose a framework to enhance existing VideoQA datasets with evidence reasoning chains.
arXiv Detail & Related papers (2025-03-17T06:30:02Z) - JOSENet: A Joint Stream Embedding Network for Violence Detection in Surveillance Videos [4.94659999696881]
Violence detection in surveillance videos presents additional issues, such as the wide variety of real fight scenes.
We introduce JOSENet, a self-supervised framework that provides outstanding performance for violence detection in surveillance videos.
arXiv Detail & Related papers (2024-05-05T15:01:00Z) - Compression effects and scene details on the source camera identification of digital videos [14.105727639288316]
It is essential to introduce forensic analysis mechanisms to guarantee the authenticity or integrity of a certain digital video.
A technique that performs the identification of the source of acquisition of digital videos from mobile devices is presented.
arXiv Detail & Related papers (2024-02-07T09:14:18Z) - Authentication and integrity of smartphone videos through multimedia
container structure analysis [9.781421596580298]
This work presents a novel technique to detect possible attacks against MP4, MOV, and 3GP format videos that affect their integrity and authenticity.
The objectives of the proposal are to verify the integrity of videos, identify the source of acquisition and distinguish between original and manipulated videos.
arXiv Detail & Related papers (2024-02-05T22:34:24Z) - AVTENet: Audio-Visual Transformer-based Ensemble Network Exploiting
Multiple Experts for Video Deepfake Detection [53.448283629898214]
The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries.
Most previous work on detecting AI-generated fake videos only utilize visual modality or audio modality.
We propose an Audio-Visual Transformer-based Ensemble Network (AVTENet) framework that considers both acoustic manipulation and visual manipulation.
arXiv Detail & Related papers (2023-10-19T19:01:26Z) - Fighting Malicious Media Data: A Survey on Tampering Detection and
Deepfake Detection [115.83992775004043]
Recent advances in deep learning, particularly deep generative models, open the doors for producing perceptually convincing images and videos at a low cost.
This paper provides a comprehensive review of the current media tampering detection approaches, and discusses the challenges and trends in this field for future research.
arXiv Detail & Related papers (2022-12-12T02:54:08Z) - Digital Image Forensics using Deep Learning [0.0]
The aim of our project is to build an algorithm that identifies which camera was used to capture an image using traces of information left intrinsically in the image.
Solving this problem would have a big impact on the verification of evidence used in criminal and civil trials and even news reporting.
arXiv Detail & Related papers (2022-10-14T02:27:34Z) - Efficient video integrity analysis through container characterization [77.45740041478743]
We introduce a container-based method to identify the software used to perform a video manipulation.
The proposed method is both efficient and effective and can also provide a simple explanation for its decisions.
It achieves an accuracy of 97.6% in distinguishing pristine from tampered videos and classifying the editing software.
arXiv Detail & Related papers (2021-01-26T14:13:39Z) - VideoForensicsHQ: Detecting High-quality Manipulated Face Videos [77.60295082172098]
We show how the performance of forgery detectors depends on the presence of artefacts that the human eye can see.
We introduce a new benchmark dataset for face video forgery detection, of unprecedented quality.
arXiv Detail & Related papers (2020-05-20T21:17:43Z) - A Modified Fourier-Mellin Approach for Source Device Identification on
Stabilized Videos [72.40789387139063]
multimedia forensic tools usually exploit characteristic noise traces left by the camera sensor on the acquired frames.
This analysis requires that the noise pattern characterizing the camera and the noise pattern extracted from video frames under analysis are geometrically aligned.
We propose to overcome this limitation by searching scaling and rotation parameters in the frequency domain.
arXiv Detail & Related papers (2020-05-20T12:06:40Z) - Multi-Modal Video Forensic Platform for Investigating Post-Terrorist
Attack Scenarios [55.82693757287532]
Large scale Video Analytic Platforms (VAP) assist law enforcement agencies (LEA) in identifying suspects and securing evidence.
We present a video analytic platform that integrates visual and audio analytic modules and fuses information from surveillance cameras and video uploads from eyewitnesses.
arXiv Detail & Related papers (2020-04-02T14:29:27Z)
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.