2022
1.
Bennabhaktula, Guru Swaroop; Alegre, Enrique; Karastoyanova, Dimka; Azzopardi, George
Camera Model Identification based on Forensic Traces extracted from Homogeneous Patches Journal Article
Abstract | Links | BibTeX | Altmetric | Tags: camera identification, forensic image analysis, image noise
@article{Bennabhaktula2022,
title = {Camera Model Identification based on Forensic Traces extracted from Homogeneous Patches},
author = {Guru Swaroop Bennabhaktula and Enrique Alegre and Dimka Karastoyanova and George Azzopardi},
doi = {https://doi.org/10.1016/j.eswa.2022.117769},
year = {2022},
date = {2022-11-15},
urldate = {2022-06-03},
journal = {Expert Systems with Applications},
volume = {206},
number = {117769},
abstract = {A crucial challenge in digital image forensics is to identify the source camera model used to generate given images. This is of prime importance, especially for Law Enforcement Agencies in their investigations of Child Sexual Abuse Material found in darknets or seized storage devices. In this work, we address this challenge by proposing a solution that is characterized by two main contributions. It relies on the extraction of rather small homogeneous regions that we extract very efficiently from the integral image, and on a hierarchical classification approach with convolutional neural networks as the underlying models. We rely on homogeneous regions as they contain camera traces that are less distorted than regions with high-level scene content. The hierarchical approach that we propose is important for scaling up and making minimal modifications when new cameras are added. Furthermore, this scheme performs better than the traditional single classifier approach. By means of thorough experimentation on the publicly available Dresden data set, we achieve an accuracy of 99.01% with 5-fold cross-validation on the `natural' subset of this data set. To the best of our knowledge, this is the best result ever reported for Dresden data set.},
keywords = {camera identification, forensic image analysis, image noise},
pubstate = {published},
tppubtype = {article}
}
A crucial challenge in digital image forensics is to identify the source camera model used to generate given images. This is of prime importance, especially for Law Enforcement Agencies in their investigations of Child Sexual Abuse Material found in darknets or seized storage devices. In this work, we address this challenge by proposing a solution that is characterized by two main contributions. It relies on the extraction of rather small homogeneous regions that we extract very efficiently from the integral image, and on a hierarchical classification approach with convolutional neural networks as the underlying models. We rely on homogeneous regions as they contain camera traces that are less distorted than regions with high-level scene content. The hierarchical approach that we propose is important for scaling up and making minimal modifications when new cameras are added. Furthermore, this scheme performs better than the traditional single classifier approach. By means of thorough experimentation on the publicly available Dresden data set, we achieve an accuracy of 99.01% with 5-fold cross-validation on the `natural' subset of this data set. To the best of our knowledge, this is the best result ever reported for Dresden data set.
2.
Bennabhaktula, Guru Swaroop; Timmerman, Derrick; Alegre, Enrique; Azzopardi, George
Source Camera Device Identification from Videos Journal Article
Abstract | Links | BibTeX | Altmetric | Tags: camera identification, constrained networks, convnets, deep learning, forensic image analysis, image noise
@article{Bennabhaktula2022b,
title = {Source Camera Device Identification from Videos},
author = {Guru Swaroop Bennabhaktula and Derrick Timmerman and Enrique Alegre and George Azzopardi},
doi = {https://doi.org/10.1007/s42979-022-01202-0},
year = {2022},
date = {2022-06-01},
urldate = {2022-06-01},
journal = {SN Computer Science},
volume = {3},
number = {316},
abstract = {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 $72.75 pm 1.1 %$ on the benchmark VISION data set. Furthermore, we also achieve state-of-the-art accuracy of $71.75%$ 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. },
keywords = {camera identification, constrained networks, convnets, deep learning, forensic image analysis, image noise},
pubstate = {published},
tppubtype = {article}
}
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 $72.75 pm 1.1 %$ on the benchmark VISION data set. Furthermore, we also achieve state-of-the-art accuracy of $71.75%$ 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.
2020
3.
Chaves, Deisy; Fidalgo, Eduardo; Alegre, Enrique; Alaiz-Rodríguez, Rocío; Jáñez-Martino, Francisco; Azzopardi, George
Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications Journal Article
Abstract | Links | BibTeX | Altmetric | Tags: convnets, deep learning, face analysis, forensic image analysis
@article{chaves2020assessment,
title = {Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications},
author = {Deisy Chaves and Eduardo Fidalgo and Enrique Alegre and Roc\'{i}o Alaiz-Rodr\'{i}guez and Francisco J\'{a}\~{n}ez-Martino and George Azzopardi},
doi = {https://doi.org/10.3390/s20164491},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Sensors},
volume = {20},
number = {4491},
issue = {16},
publisher = {2020},
abstract = {Face recognition is a valuable forensic tool for criminal investigators since it certainly helps in identifying individuals in scenarios of criminal activity like fugitives or child sexual abuse. It is, however, a very challenging task as it must be able to handle low-quality images of real world settings and fulfill real time requirements. Deep learning approaches for face detection have proven to be very successful but they require large computation power and processing time. In this work, we evaluate the speed\textendashaccuracy tradeoff of three popular deep-learning-based face detectors on the WIDER Face and UFDD data sets in several CPUs and GPUs. We also develop a regression model capable to estimate the performance, both in terms of processing time and accuracy. We expect this to become a very useful tool for the end user in forensic laboratories in order to estimate the performance for different face detection options. Experimental results showed that the best speed\textendashaccuracy tradeoff is achieved with images resized to 50% of the original size in GPUs and images resized to 25% of the original size in CPUs. Moreover, performance can be estimated using multiple linear regression models with a Mean Absolute Error (MAE) of 0.113, which is very promising for the forensic field.},
keywords = {convnets, deep learning, face analysis, forensic image analysis},
pubstate = {published},
tppubtype = {article}
}
Face recognition is a valuable forensic tool for criminal investigators since it certainly helps in identifying individuals in scenarios of criminal activity like fugitives or child sexual abuse. It is, however, a very challenging task as it must be able to handle low-quality images of real world settings and fulfill real time requirements. Deep learning approaches for face detection have proven to be very successful but they require large computation power and processing time. In this work, we evaluate the speed–accuracy tradeoff of three popular deep-learning-based face detectors on the WIDER Face and UFDD data sets in several CPUs and GPUs. We also develop a regression model capable to estimate the performance, both in terms of processing time and accuracy. We expect this to become a very useful tool for the end user in forensic laboratories in order to estimate the performance for different face detection options. Experimental results showed that the best speed–accuracy tradeoff is achieved with images resized to 50% of the original size in GPUs and images resized to 25% of the original size in CPUs. Moreover, performance can be estimated using multiple linear regression models with a Mean Absolute Error (MAE) of 0.113, which is very promising for the forensic field.