Video Camera Identification from Sensor Pattern Noise with a Constrained ConvNet

• URL: http://arxiv.org/abs/2012.06277v1
• Date: Fri, 11 Dec 2020 12:17:30 GMT
• Title: Video Camera Identification from Sensor Pattern Noise with a Constrained ConvNet
• Authors: Derrick Timmerman, Swaroop Bennabhaktula, Enrique Alegre and George Azzopardi
• Abstract summary: We propose a method to identify the source camera of a video based on camera specific noise patterns that we extract from video frames. Our system is designed to classify individual video frames which are in turn combined by a majority vote to identify the source camera. This work is part of the EU-funded project 4NSEEK focused on forensics against child sexual abuse.
• Score: 7.229968041355052
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The identification of source cameras from videos, though it is a highly relevant forensic analysis topic, has been studied much less than its counterpart that uses images. In this work we propose a method to identify the source camera of a video based on camera specific noise patterns that we extract from video frames. For the extraction of noise pattern features, we propose an extended version of a constrained convolutional layer capable of processing color inputs. Our system is designed to classify individual video frames which are in turn combined by a majority vote to identify the source camera. We evaluated this approach on the benchmark VISION data set consisting of 1539 videos from 28 different cameras. To the best of our knowledge, this is the first work that addresses the challenge of video camera identification on a device level. The experiments show that our approach is very promising, achieving up to 93.1% accuracy while being robust to the WhatsApp and YouTube compression techniques. This work is part of the EU-funded project 4NSEEK focused on forensics against child sexual abuse.

Related papers

• Temporal and Contextual Transformer for Multi-Camera Editing of TV Shows [83.54243912535667]
  We first collect a novel benchmark on this setting with four diverse scenarios including concerts, sports games, gala shows, and contests. It contains 88-hour raw videos that contribute to the 14-hour edited videos. We propose a new approach temporal and contextual transformer that utilizes clues from historical shots and other views to make shot transition decisions.
  arXiv  Detail & Related papers  (2022-10-17T04:11:23Z)
• 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)
• PRNU Based Source Camera Identification for Webcam and Smartphone Videos [137.6408511310322]
  This communication is about an application of image forensics where we use camera sensor fingerprints to identify source camera (SCI: Source Camera Identification) in webcam/smartphone videos.
  arXiv  Detail & Related papers  (2022-01-27T18:57:14Z)
• A new Video Synopsis Based Approach Using Stereo Camera [0.5801044612920815]
  A new method for anomaly detection with object-based unsupervised learning has been developed. By using this method, the video data is processed as pixels and the result is produced as a video segment. The model we developed has been tested and verified separately for single camera and dual camera systems.
  arXiv  Detail & Related papers  (2021-06-23T12:57:47Z)
• 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)
• The Forchheim Image Database for Camera Identification in the Wild [10.091921099426294]
  Forchheim Image Database (FODB) consists of more than 23,000 images of 143 scenes by 27 smartphone cameras. Each image is provided in 6 different qualities: the original camera-native version, and five copies from social networks. General-purpose EfficientNet remarkably outperforms several dedicated forensic CNNs both on clean and compressed images.
  arXiv  Detail & Related papers  (2020-11-04T11:54:54Z)
• A leak in PRNU based source identification. Questioning fingerprint uniqueness [75.33542585238497]
  Photo Response Non-Uniformity (PRNU) is considered the most effective trace for the image source attribution task. Recent devices may introduce non-unique artifacts that may reduce PRNU noise's distinctiveness. We show that the primary cause of high false alarm rates cannot be directly related to specific camera models, firmware, or image contents.
  arXiv  Detail & Related papers  (2020-09-10T14:18:38Z)
• 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)
• Device-based Image Matching with Similarity Learning by Convolutional Neural Networks that Exploit the Underlying Camera Sensor Pattern Noise [6.6602878519516135]
  We propose a two-part network to quantify the likelihood that a given pair of images have the same source camera. To the best of our knowledge, we are the first ones addressing the challenge of device-based image matching. This work is part of the EU-funded project 4NSEEK concerned with forensics against child sexual abuse.
  arXiv  Detail & Related papers  (2020-04-23T20:03:40Z)
• Camera Trace Erasing [86.15997461603568]
  We address a new low-level vision problem, camera trace erasing, to reveal the weakness of trace-based forensic methods. We propose Siamese Trace Erasing (SiamTE), in which a novel hybrid loss is designed on the basis of Siamese architecture for network training.
  arXiv  Detail & Related papers  (2020-03-16T00:09:55Z)

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.