Video Camera Identification from Sensor Pattern Noise with a Constrained ConvNet

Timmerman, Derrick; Bennabhaktula, Guru Swaroop; Alegre, Enrique; Azzopardi, George

doi:10.5220/0010246804170425

Cited by 8 publications

(9 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors show that using such a filter before frame averaging helps in suppressing scene content. This idea is similar to more recent works [6,52] that allow the neural networks to learn constrained filters for scene suppression.…”

Section: Related Workmentioning

confidence: 74%

“…Firstly, methods that use predefined high-pass filtering to suppress high-level scene content [45]. Secondly, methods that use constrained convolutions [5,52], which consist of trainable filter parameters.…”

Section: Related Workmentioning

confidence: 99%

“…That layer was removed as it was not compatible to handle color images or multi-channel inputs. Derrick et al [52] proposed an extended version of the constrained convolution layer and depict the scenarios in which inclusion of multi-channel constrained layer could be beneficial. All these works show the effectiveness of the constrained convolutional layer when used with shallow ConvNets.…”

Section: Related Workmentioning

confidence: 99%

“…As most of the digital media is generated as color or multi-channel, by converting them to monochrome, the native information that is present in each individual color channel is lost. To overcome this problem, an extended version of the constrained convolutional layer that can handle multi-channel inputs was proposed in [52]. These methods demonstrate the usefulness of constrained convolutional layer for shallow ConvNets.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Source Camera Device Identification from Videos

et al. 2022

Self Cite

View full text Add to dashboard Cite

Source camera identification is an important and challenging problem in digital image forensics. The clues of the device used to capture the digital media are very useful for Law Enforcement Agencies (LEAs), especially to help them collect more intelligence in digital forensics. In our work, we focus on identifying the source camera device based on digital videos using deep learning methods. In particular, we evaluate deep learning models with increasing levels of complexity for source camera identification and show that with such sophistication the scene-suppression techniques do not aid in model performance. In addition, we mention several common machine learning strategies that are counter-productive in achieving a high accuracy for camera identification. We conduct systematic experiments using 28 devices from the VISION data set and evaluate the model performance on various video scenarios—flat (i.e., homogeneous), indoor, and outdoor and evaluate the impact on classification accuracy when the videos are shared via social media platforms such as YouTube and WhatsApp. Unlike traditional PRNU-noise (Photo Response Non-Uniform)-based methods which require flat frames to estimate camera reference pattern noise, the proposed method has no such constraint and we achieve an accuracy of $$72.75 \pm 1.1 \%$$ 72.75 ± 1.1 % on the benchmark VISION data set. Furthermore, we also achieve state-of-the-art accuracy of $$71.75\%$$ 71.75 % on the QUFVD data set in identifying 20 camera devices. These two results are the best ever reported on the VISION and QUFVD data sets. Finally, we demonstrate the runtime efficiency of the proposed approach and its advantages to LEAs.

show abstract

Section: Related Workmentioning

confidence: 74%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Source Camera Device Identification from Videos

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…The method presented in [29] is used to evaluate our database. Also, in references [30], [31] and [23], the CNN method (MISLnet CNN architecture [29]) was used to identify the source camera, using frames to train the network. The network used a constrained convolutional layer that was added as the first layer that used three kernels with size 5.…”

Section: C) Content Propertiesmentioning

confidence: 99%

A New Forensic Video Database for Source Smartphone Identification: Description and Analysis

et al. 2022

View full text Add to dashboard Cite

In recent years, the field of digital imaging has made significant progress, so that today every smartphone has a built-in video camera that allows you to record high-quality video for free and without restrictions. On the other hand, rapidly growing internet technology has contributed significantly to the widespread use of digital video via web-based multimedia systems and mobile smartphone applications such as YouTube, Facebook, Twitter, WhatsApp, etc. However, as the recording and distribution of digital videos have become affordable nowadays, security issues have become threatening and spread worldwide. One of the security issues is identifying source cameras on videos. There are some new challenges that should be addressed in this area. One of the new challenges is individual source camera identification (ISCI), which focuses on identifying each device regardless of its model. The first step towards solving the problems is a popular video database recorded by modern smartphone devices, which can also be used for deep learning methods that are growing rapidly in the field of source camera identification. In this paper, a smartphone video database named Qatar University Forensic Video Database (QUFVD) is introduced. The QUFVD includes 6000 videos from 20 modern smartphone representing five brands, each brand has two models, and each model has two identical smartphone devices. This database is suitable for evaluating different techniques such as deep learning methods for video source smartphone identification and verification. To evaluate the QUFVD , a series of experiments to identify source cameras using a deep learning technique are conducted. The results show that improvements are essential for the ISCI scenario on video.

show abstract

Deep Learning Image Age Approximation - What is More Relevant: Image Content or Age Information?

Jochl

Uhl

2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Video Camera Identification from Sensor Pattern Noise with a Constrained ConvNet

Cited by 8 publications

References 0 publications

Source Camera Device Identification from Videos

Source Camera Device Identification from Videos

A New Forensic Video Database for Source Smartphone Identification: Description and Analysis

Deep Learning Image Age Approximation - What is More Relevant: Image Content or Age Information?

Contact Info

Product

Resources

About