Using Deep Learning Methods for Forensic Image and Video Investigation (original) (raw)

Review on effectiveness of deep learning approach in digital forensics

Photosynthesis optimization, 2022

Cyber forensics is use of scientific methods for definite description of cybercrime activities. It deals with collecting, processing and interpreting digital evidence for cybercrime analysis. Cyber forensic analysis plays very important role in criminal investigations. Although lot of research has been done in cyber forensics, it is still expected to face new challenges in near future. Analysis of digital media specifically photographic images, audio and video recordings are very crucial in forensics This paper specifically focus on digital forensics. There are several methods for digital forensic analysis. Currently deep learning (DL), mainly convolutional neural network (CNN) has proved very promising in classification of digital images and sound analysis techniques. This paper presents a compendious study of recent research and methods in forensic areas based on CNN, with a view to guide the researchers working in this area. We first, defined and explained preliminary models of DL. In the next section, out of several DL models we have focused on CNN and its usage in areas of digital forensic. Finally, conclusion and future work are discussed. The review shows that CNN has proved good in most of the forensic domains and still promise to be better.

Application of Machine Learning In Forensic Science

Advances in Digital Crime, Forensics, and Cyber Terrorism, 2020

In this chapter, the authors explore the use of machine learning methodology for cyber forensics as machine learning has proven its importance and efficiency. For classification and identification purposes in forensic science, pattern recognition algorithms can be very helpful.

A survey of deepfakes in terms of deep learning and multimedia forensics

International Journal of Electrical and Computer Engineering (IJECE), 2022

Artificial intelligence techniques are reaching us in several forms, some of which are useful but can be exploited in a way that harms us. One of these forms is called deepfakes. Deepfakes is used to completely modify video (or image) content to display something that was not in it originally. The danger of deepfake technology impact on society through the loss of confidence in everything is published. Therefore, in this paper, we focus on deepfake detection technology from the view of two concepts which are deep learning and forensic tools. The purpose of this survey is to give the reader a deeper overview of i) the environment of deepfake creation and detection, ii) how deep learning and forensic tools contributed to the detection of deepfakes, and iii) finally how in the future incorporating both deep learning technology and tools for forensics can increase the efficiency of deepfakes detection.

Non-Facial Video Spatiotemporal Forensic Analysis Using Deep Learning Techniques

Proceedings of Engineering and Technology Innovation

Digital content manipulation software is working as a boon for people to edit recorded video or audio content. To prevent the unethical use of such readily available altering tools, digital multimedia forensics is becoming increasingly important. Hence, this study aims to identify whether the video and audio of the given digital content are fake or real. For temporal video forgery detection, the convolutional 3D layers are used to build a model which can identify temporal forgeries with an average accuracy of 85% on the validation dataset. Also, the identification of audio forgery, using a ResNet-34 pre-trained model and the transfer learning approach, has been achieved. The proposed model achieves an accuracy of 99% with 0.3% validation loss on the validation part of the logical access dataset, which is better than earlier models in the range of 90-95% accuracy on the validation set.

Use of Advanced Artificial Intelligence in Forensic Medicine

2021

Three-dimensional convolutional neural networks (3D CNN) as a type of artificial intelligence (AI) are powerful in image processing and recognition using deep learning to perform generative and descriptive tasks. The advantage of CNN compared to its predecessors is that it automatically detects the important features without any human supervision. 3D CNN are used to extract features in three dimensions where input is a 3D volume or a sequence of 2D pictures e.g., slices in a cone-beam computer tomography scan (CBCT). The main aim of this article was to bridge interdisciplinary cooperation between forensic medical experts and deep learning engineers. With emphasis activating clinical forensic experts in the field with possibly basic knowledge of advanced artificial intelligence techniques with interest in its implementation in their efforts to advance the forensic research further. This paper introduces a novel workflow of 3D CNN analysis of full-head CBCT scans. Authors explore and ...

Data mining approach for digital forensics task with deep learning techniques

International Journal of Advanced and Applied Sciences, 2020

In the past, digital forensic, with its exploration techniques, are a lane to the data recovery as well as the examination of different investigation techniques. It is a line of investigation which includes many stages. In this, the foremost assignment is data collection later than that the outcome amount produced predicted with the dataset. Some authors proposed several supervised machine learning techniques that have not obtained much better results. Therefore, the goal of our study was to perform an investigational work on a forensics dataset task for class-based classification methods like three-layer CNN classifiers, five-layer CNN classifiers, and seven-layer CNN classifiers. The classifiers evaluated with classification performance and accuracy. The experimental plan has been done with fivefold cross-validation with fifty repetitions for deep learning algorithms in order to obtain consistent results. Matching accuracy values for the next to next pixels in the classes are calculated with the class-based predicted labels. There are four classes assigned on CNN, and the four classes are segmented and separated with the same region of interest. Then the same class-based region of interests is segregated, and these four class-based regions are next given to CNN with the clusters. Further, the comparison results are made with the used three algorithms.

MS Forensics 2018-Book of Abstracts

2018

The Drugs Working Group of the European Network of Forensic Science Institutes consists of the Steering Committee and several Sub Committees. The Group supports the aims and objectives in the area of forensic drug examination. The members of the Group continuously work on fulfilment of the aims and international standards. For that purpose several levels of communication and exchange of information had been established. Members of the Group are involved in the particular projects aiming to straighten capacities of the own laboratories, but also to contribute the ENFSI community. A number of comprehensive tools and products had been created by the Group members such as recommendations, software and guidelines. Accreditation status of the labs and conducting of PT is of great concern in the Group as well. The continual education is conducted within the Group through the different workshops. The collaboration with guest organizations like European Monitoring Centre for Drugs and Drug Addiction, Scientific Working Group for Drugs and United Nations Office on Drugs and Crime, gives the frame of the global and overall attempt to contribute in the field of fight against drugs.

A Dataset of Photos and Videos for Digital Forensics Analysis Using Machine Learning Processing

Data, 2021

Deepfake and manipulated digital photos and videos are being increasingly used in a myriad of cybercrimes. Ransomware, the dissemination of fake news, and digital kidnapping-related crimes are the most recurrent, in which tampered multimedia content has been the primordial disseminating vehicle. Digital forensic analysis tools are being widely used by criminal investigations to automate the identification of digital evidence in seized electronic equipment. The number of files to be processed and the complexity of the crimes under analysis have highlighted the need to employ efficient digital forensics techniques grounded on state-of-the-art technologies. Machine Learning (ML) researchers have been challenged to apply techniques and methods to improve the automatic detection of manipulated multimedia content. However, the implementation of such methods have not yet been massively incorporated into digital forensic tools, mostly due to the lack of realistic and well-structured dataset...

The Role of Artificial Intelligence in Forensic Science: Transforming Investigations through Technology

IJMRAP, 2024

Artificial intelligence transforms forensic science today by offering an unprecedented breadth of better precision and speed in criminal investigations. Artificial intelligence (AI) technologies that include reconstruction of crime scenes, DNA analysis, digital forensics, and many others applied to forensic science will have farreaching impacts on the field. This review discusses the use of AI in transforming a number of the disciplines in forensic sciences: namely, crime scene investigation, pattern recognition and forensic toxicology, among others; its challenges, ethical and legal considerations. This article finally emphasizes the future of AI in forensic science, as well as its integration with other emerging technologies.