Visible to the public Biblio

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2021-05-13
Zhang, Mingyue, Zhou, Junlong, Cao, Kun, Hu, Shiyan.  2020.  Trusted Anonymous Authentication For Vehicular Cyber-Physical Systems. 2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics). :37—44.
In vehicular cyber-physical systems, the mounted cameras on the vehicles, together with the fixed roadside cameras, can produce pictorial data for multiple purposes. In this process, ensuring the security and privacy of vehicles while guaranteeing efficient data transmission among vehicles is critical. This motivates us to propose a trusted anonymous authentication scheme for vehicular cyber-physical systems and Internet-of-Things. Our scheme is designed based on a three-tier architecture which contains cloud layer, fog layer, and user layer. It utilizes bilinear-free certificateless signcryption to realize a secure and trusted anonymous authentication efficiently. We verify its effectiveness through theoretical analyses in terms of correctness, security, and efficiency. Furthermore, our simulation results demonstrate that the communication overhead, the computation overhead, and the packet loss rate of the proposed scheme are significantly better than those of the state-of-the-art techniques. Particularly, the proposed scheme can speed up the computation process at least 10× compared to all the state-of-the-art approaches.
2021-05-05
Tabiban, Azadeh, Jarraya, Yosr, Zhang, Mengyuan, Pourzandi, Makan, Wang, Lingyu, Debbabi, Mourad.  2020.  Catching Falling Dominoes: Cloud Management-Level Provenance Analysis with Application to OpenStack. 2020 IEEE Conference on Communications and Network Security (CNS). :1—9.

The dynamicity and complexity of clouds highlight the importance of automated root cause analysis solutions for explaining what might have caused a security incident. Most existing works focus on either locating malfunctioning clouds components, e.g., switches, or tracing changes at lower abstraction levels, e.g., system calls. On the other hand, a management-level solution can provide a big picture about the root cause in a more scalable manner. In this paper, we propose DOMINOCATCHER, a novel provenance-based solution for explaining the root cause of security incidents in terms of management operations in clouds. Specifically, we first define our provenance model to capture the interdependencies between cloud management operations, virtual resources and inputs. Based on this model, we design a framework to intercept cloud management operations and to extract and prune provenance metadata. We implement DOMINOCATCHER on OpenStack platform as an attached middleware and validate its effectiveness using security incidents based on real-world attacks. We also evaluate the performance through experiments on our testbed, and the results demonstrate that DOMINOCATCHER incurs insignificant overhead and is scalable for clouds.

2021-04-08
Zhang, J., Liao, Y., Zhu, X., Wang, H., Ding, J..  2020.  A Deep Learning Approach in the Discrete Cosine Transform Domain to Median Filtering Forensics. IEEE Signal Processing Letters. 27:276—280.
This letter presents a novel median filtering forensics approach, based on a convolutional neural network (CNN) with an adaptive filtering layer (AFL), which is built in the discrete cosine transform (DCT) domain. Using the proposed AFL, the CNN can determine the main frequency range closely related with the operational traces. Then, to automatically learn the multi-scale manipulation features, a multi-scale convolutional block is developed, exploring a new multi-scale feature fusion strategy based on the maxout function. The resultant features are further processed by a convolutional stream with pooling and batch normalization operations, and finally fed into the classification layer with the Softmax function. Experimental results show that our proposed approach is able to accurately detect the median filtering manipulation and outperforms the state-of-the-art schemes, especially in the scenarios of low image resolution and serious compression loss.
Mayer, O., Stamm, M. C..  2020.  Forensic Similarity for Digital Images. IEEE Transactions on Information Forensics and Security. 15:1331—1346.
In this paper, we introduce a new digital image forensics approach called forensic similarity, which determines whether two image patches contain the same forensic trace or different forensic traces. One benefit of this approach is that prior knowledge, e.g., training samples, of a forensic trace is not required to make a forensic similarity decision on it in the future. To do this, we propose a two-part deep-learning system composed of a convolutional neural network-based feature extractor and a three-layer neural network, called the similarity network. This system maps the pairs of image patches to a score indicating whether they contain the same or different forensic traces. We evaluated the system accuracy of determining whether two image patches were captured by the same or different camera model and manipulated by the same or a different editing operation and the same or a different manipulation parameter, given a particular editing operation. Experiments demonstrate applicability to a variety of forensic traces and importantly show efficacy on “unknown” forensic traces that were not used to train the system. Experiments also show that the proposed system significantly improves upon prior art, reducing error rates by more than half. Furthermore, we demonstrated the utility of the forensic similarity approach in two practical applications: forgery detection and localization, and database consistency verification.
Guerrini, F., Dalai, M., Leonardi, R..  2020.  Minimal Information Exchange for Secure Image Hash-Based Geometric Transformations Estimation. IEEE Transactions on Information Forensics and Security. 15:3482—3496.
Signal processing applications dealing with secure transmission are enjoying increasing attention lately. This paper provides some theoretical insights as well as a practical solution for transmitting a hash of an image to a central server to be compared with a reference image. The proposed solution employs a rigid image registration technique viewed in a distributed source coding perspective. In essence, it embodies a phase encoding framework to let the decoder estimate the transformation parameters using a very modest amount of information about the original image. The problem is first cast in an ideal setting and then it is solved in a realistic scenario, giving more prominence to low computational complexity in both the transmitter and receiver, minimal hash size, and hash security. Satisfactory experimental results are reported on a standard images set.
Zheng, Y., Cao, Y., Chang, C..  2020.  A PUF-Based Data-Device Hash for Tampered Image Detection and Source Camera Identification. IEEE Transactions on Information Forensics and Security. 15:620—634.
With the increasing prevalent of digital devices and their abuse for digital content creation, forgeries of digital images and video footage are more rampant than ever. Digital forensics is challenged into seeking advanced technologies for forgery content detection and acquisition device identification. Unfortunately, existing solutions that address image tampering problems fail to identify the device that produces the images or footage while techniques that can identify the camera is incapable of locating the tampered content of its captured images. In this paper, a new perceptual data-device hash is proposed to locate maliciously tampered image regions and identify the source camera of the received image data as a non-repudiable attestation in digital forensics. The presented image may have been either tampered or gone through benign content preserving geometric transforms or image processing operations. The proposed image hash is generated by projecting the invariant image features into a physical unclonable function (PUF)-defined Bernoulli random space. The tamper-resistant random PUF response is unique for each camera and can only be generated upon triggered by a challenge, which is provided by the image acquisition timestamp. The proposed hash is evaluated on the modified CASIA database and CMOS image sensor-based PUF simulated using 180 nm TSMC technology. It achieves a high tamper detection rate of 95.42% with the regions of tampered content successfully located, a good authentication performance of above 98.5% against standard content-preserving manipulations, and 96.25% and 90.42%, respectively, for the more challenging geometric transformations of rotation (0 360°) and scaling (scale factor in each dimension: 0.5). It is demonstrated to be able to identify the source camera with 100% accuracy and is secure against attacks on PUF.
Verdoliva, L..  2020.  Media Forensics and DeepFakes: An Overview. IEEE Journal of Selected Topics in Signal Processing. 14:910—932.
With the rapid progress in recent years, techniques that generate and manipulate multimedia content can now provide a very advanced level of realism. The boundary between real and synthetic media has become very thin. On the one hand, this opens the door to a series of exciting applications in different fields such as creative arts, advertising, film production, and video games. On the other hand, it poses enormous security threats. Software packages freely available on the web allow any individual, without special skills, to create very realistic fake images and videos. These can be used to manipulate public opinion during elections, commit fraud, discredit or blackmail people. Therefore, there is an urgent need for automated tools capable of detecting false multimedia content and avoiding the spread of dangerous false information. This review paper aims to present an analysis of the methods for visual media integrity verification, that is, the detection of manipulated images and videos. Special emphasis will be placed on the emerging phenomenon of deepfakes, fake media created through deep learning tools, and on modern data-driven forensic methods to fight them. The analysis will help highlight the limits of current forensic tools, the most relevant issues, the upcoming challenges, and suggest future directions for research.
Yang, Z., Sun, Q., Zhang, Y., Zhu, L., Ji, W..  2020.  Inference of Suspicious Co-Visitation and Co-Rating Behaviors and Abnormality Forensics for Recommender Systems. IEEE Transactions on Information Forensics and Security. 15:2766—2781.
The pervasiveness of personalized collaborative recommender systems has shown the powerful capability in a wide range of E-commerce services such as Amazon, TripAdvisor, Yelp, etc. However, fundamental vulnerabilities of collaborative recommender systems leave space for malicious users to affect the recommendation results as the attackers desire. A vast majority of existing detection methods assume certain properties of malicious attacks are given in advance. In reality, improving the detection performance is usually constrained due to the challenging issues: (a) various types of malicious attacks coexist, (b) limited representations of malicious attack behaviors, and (c) practical evidences for exploring and spotting anomalies on real-world data are scarce. In this paper, we investigate a unified detection framework in an eye for an eye manner without being bothered by the details of the attacks. Firstly, co-visitation and co-rating graphs are constructed using association rules. Then, attribute representations of nodes are empirically developed from the perspectives of linkage pattern, structure-based property and inherent association of nodes. Finally, both attribute information and connective coherence of graph are combined in order to infer suspicious nodes. Extensive experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed detection approach compared with competing benchmarks. Additionally, abnormality forensics metrics including distribution of rating intention, time aggregation of suspicious ratings, degree distributions before as well as after removing suspicious nodes and time series analysis of historical ratings, are provided so as to discover interesting findings such as suspicious nodes (items or ratings) on real-world data.
2021-03-04
Carlini, N., Farid, H..  2020.  Evading Deepfake-Image Detectors with White- and Black-Box Attacks. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). :2804—2813.

It is now possible to synthesize highly realistic images of people who do not exist. Such content has, for example, been implicated in the creation of fraudulent socialmedia profiles responsible for dis-information campaigns. Significant efforts are, therefore, being deployed to detect synthetically-generated content. One popular forensic approach trains a neural network to distinguish real from synthetic content.We show that such forensic classifiers are vulnerable to a range of attacks that reduce the classifier to near- 0% accuracy. We develop five attack case studies on a state- of-the-art classifier that achieves an area under the ROC curve (AUC) of 0.95 on almost all existing image generators, when only trained on one generator. With full access to the classifier, we can flip the lowest bit of each pixel in an image to reduce the classifier's AUC to 0.0005; perturb 1% of the image area to reduce the classifier's AUC to 0.08; or add a single noise pattern in the synthesizer's latent space to reduce the classifier's AUC to 0.17. We also develop a black-box attack that, with no access to the target classifier, reduces the AUC to 0.22. These attacks reveal significant vulnerabilities of certain image-forensic classifiers.

2021-02-23
Patil, A., Jha, A., Mulla, M. M., Narayan, D. G., Kengond, S..  2020.  Data Provenance Assurance for Cloud Storage Using Blockchain. 2020 International Conference on Advances in Computing, Communication Materials (ICACCM). :443—448.

Cloud forensics investigates the crime committed over cloud infrastructures like SLA-violations and storage privacy. Cloud storage forensics is the process of recording the history of the creation and operations performed on a cloud data object and investing it. Secure data provenance in the Cloud is crucial for data accountability, forensics, and privacy. Towards this, we present a Cloud-based data provenance framework using Blockchain, which traces data record operations and generates provenance data. Initially, we design a dropbox like application using AWS S3 storage. The application creates a cloud storage application for the students and faculty of the university, thereby making the storage and sharing of work and resources efficient. Later, we design a data provenance mechanism for confidential files of users using Ethereum blockchain. We also evaluate the proposed system using performance parameters like query and transaction latency by varying the load and number of nodes of the blockchain network.

2021-01-15
Zeid, R. B., Moubarak, J., Bassil, C..  2020.  Investigating The Darknet. 2020 International Wireless Communications and Mobile Computing (IWCMC). :727—732.

Cybercrime is growing dramatically in the technological world nowadays. World Wide Web criminals exploit the personal information of internet users and use them to their advantage. Unethical users leverage the dark web to buy and sell illegal products or services and sometimes they manage to gain access to classified government information. A number of illegal activities that can be found in the dark web include selling or buying hacking tools, stolen data, digital fraud, terrorists activities, drugs, weapons, and more. The aim of this project is to collect evidence of any malicious activity in the dark web by using computer security mechanisms as traps called honeypots.

McCloskey, S., Albright, M..  2019.  Detecting GAN-Generated Imagery Using Saturation Cues. 2019 IEEE International Conference on Image Processing (ICIP). :4584—4588.
Image forensics is an increasingly relevant problem, as it can potentially address online disinformation campaigns and mitigate problematic aspects of social media. Of particular interest, given its recent successes, is the detection of imagery produced by Generative Adversarial Networks (GANs), e.g. `deepfakes'. Leveraging large training sets and extensive computing resources, recent GANs can be trained to generate synthetic imagery which is (in some ways) indistinguishable from real imagery. We analyze the structure of the generating network of a popular GAN implementation [1], and show that the network's treatment of exposure is markedly different from a real camera. We further show that this cue can be used to distinguish GAN-generated imagery from camera imagery, including effective discrimination between GAN imagery and real camera images used to train the GAN.
Khalid, H., Woo, S. S..  2020.  OC-FakeDect: Classifying Deepfakes Using One-class Variational Autoencoder. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). :2794—2803.
An image forgery method called Deepfakes can cause security and privacy issues by changing the identity of a person in a photo through the replacement of his/her face with a computer-generated image or another person's face. Therefore, a new challenge of detecting Deepfakes arises to protect individuals from potential misuses. Many researchers have proposed various binary-classification based detection approaches to detect deepfakes. However, binary-classification based methods generally require a large amount of both real and fake face images for training, and it is challenging to collect sufficient fake images data in advance. Besides, when new deepfakes generation methods are introduced, little deepfakes data will be available, and the detection performance may be mediocre. To overcome these data scarcity limitations, we formulate deepfakes detection as a one-class anomaly detection problem. We propose OC-FakeDect, which uses a one-class Variational Autoencoder (VAE) to train only on real face images and detects non-real images such as deepfakes by treating them as anomalies. Our preliminary result shows that our one class-based approach can be promising when detecting Deepfakes, achieving a 97.5% accuracy on the NeuralTextures data of the well-known FaceForensics++ benchmark dataset without using any fake images for the training process.
2020-12-17
Abeykoon, I., Feng, X..  2019.  Challenges in ROS Forensics. 2019 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). :1677—1682.

The usage of robot is rapidly growth in our society. The communication link and applications connect the robots to their clients or users. This communication link and applications are normally connected through some kind of network connections. This network system is amenable of being attached and vulnerable to the security threats. It is a critical part for ensuring security and privacy for robotic platforms. The paper, also discusses about several cyber-physical security threats that are only for robotic platforms. The peer to peer applications use in the robotic platforms for threats target integrity, availability and confidential security purposes. A Remote Administration Tool (RAT) was introduced for specific security attacks. An impact oriented process was performed for analyzing the assessment outcomes of the attacks. Tests and experiments of attacks were performed in simulation environment which was based on Gazbo Turtlebot simulator and physically on the robot. A software tool was used for simulating, debugging and experimenting on ROS platform. Integrity attacks performed for modifying commands and manipulated the robot behavior. Availability attacks were affected for Denial-of-Service (DoS) and the robot was not listened to Turtlebot commands. Integrity and availability attacks resulted sensitive information on the robot.

Basheer, M. M., Varol, A..  2019.  An Overview of Robot Operating System Forensics. 2019 1st International Informatics and Software Engineering Conference (UBMYK). :1—4.
Autonomous technologies have been rapidly replacing the traditional manual intervention nearly in every aspect of our life. These technologies essentially require robots to carry out their automated processes. Nowadays, with the emergence of industry 4.0, robots are increasingly being remote-controlled via client-server connection, which creates uncommon vulnerabilities that allow attackers to target those robots. The development of an open source operational environment for robots, known as Robot Operating System (ROS) has come as a response to these demands. Security and privacy are crucial for the use of ROS as the chance of a compromise may lead to devastating ramifications. In this paper, an overview of ROS and the attacks targeting it are detailed and discussed. Followed by a review of the ROS security and digital investigation studies.
2020-09-04
Wu, Yan, Luo, Anthony, Xu, Dianxiang.  2019.  Forensic Analysis of Bitcoin Transactions. 2019 IEEE International Conference on Intelligence and Security Informatics (ISI). :167—169.
Bitcoin [1] as a popular digital currency has been a target of theft and other illegal activities. Key to the forensic investigation is to identify bitcoin addresses involved in bitcoin transfers. This paper presents a framework, FABT, for forensic analysis of bitcoin transactions by identifying suspicious bitcoin addresses. It formalizes the clues of a given case as transaction patterns defined over a comprehensive set of features. FABT converts the bitcoin transaction data into a formal model, called Bitcoin Transaction Net (BTN). The traverse of all bitcoin transactions in the order of their occurrences is captured by the firing sequence of all transitions in the BTN. We have applied FABT to identify suspicious addresses in the Mt.Gox case. A subgroup of the suspicious addresses has been found to share many characteristics about the received/transferred amount, number of transactions, and time intervals.
2020-08-28
Kommera, Nikitha, Kaleem, Faisal, Shah Harooni, Syed Mubashir.  2016.  Smart augmented reality glasses in cybersecurity and forensic education. 2016 IEEE Conference on Intelligence and Security Informatics (ISI). :279—281.
Augmented reality is changing the way its users see the world. Smart augmented-reality glasses, with high resolution Optical Head Mounted display, supplements views of the real-world using video, audio, or graphics projected in front of user's eye. The area of Smart Glasses and heads-up display devices is not a new one, however in the last few years, it has seen an extensive growth in various fields including education. Our work takes advantage of a student's ability to adapt to new enabling technologies to investigate improvements teaching techniques in STEM areas and enhance the effectiveness and efficiency in teaching the new course content. In this paper, we propose to focus on the application of Smart Augmented-Reality Glasses in cybersecurity education to attract and retain students in STEM. In addition, creative ways to learn cybersecurity education via Smart Glasses will be explored using a Discovery Learning approach. This mode of delivery will allow students to interact with cybersecurity theories in an innovative, interactive and effective way, enhancing their overall live experience and experimental learning. With the help of collected data and in-depth analysis of existing smart glasses, the ongoing work will lay the groundwork for developing augmented reality applications that will enhance the learning experiences of students. Ultimately, research conducted with the glasses and applications may help to identify the unique skillsets of cybersecurity analysts, learning gaps and learning solutions.
2020-07-30
Bays, Jason, Karabiyik, Umit.  2019.  Forensic Analysis of Third Party Location Applications in Android and iOS. IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). :1—6.
Location sharing applications are becoming increasingly common. These applications allow users to share their own locations and view contacts’ current locations on a map. Location applications are commonly used by friends and family members to view Global Positioning System (GPS) location of an individual, but valuable forensic evidence may exist in this data when stored locally on smartphones. This paper aims to discover forensic artifacts from two popular third-party location sharing applications on iOS and Android devices. Industry standard mobile forensic suites are utilized to discover if any locally stored data could be used to assist investigations reliant on knowing the past location of a suspect. Security issues raised regarding the artifacts found during our analysis is also discussed.
2020-07-27
Sudozai, M. A. K., Saleem, Shahzad.  2018.  Profiling of secure chat and calling apps from encrypted traffic. 2018 15th International Bhurban Conference on Applied Sciences and Technology (IBCAST). :502–508.
Increased use of secure chat and voice/ video apps has transformed the social life. While the benefits and facilitations are seemingly limitless, so are the asscoiacted vulnerabilities and threats. Besides ensuring confidentiality requirements for common users, known facts of non-readable contents over the network make these apps more attractive for criminals. Though access to contents of cryptograhically secure sessions is not possible, network forensics of secure apps can provide interesting information which can be of great help during criminal invetigations. In this paper, we presented a novel framework of profiling the secure chat and voice/ video calling apps which can be employed to extract hidden patterns about the app, information of involved parties, activities of chatting, voice/ video calls, status indications and notifications while having no information of communication protocol of the app and its security architecture. Signatures of any secure app can be developed though our framework and can become base of a large scale solution. Our methodology is considered very important for different cases of criminal investigations and bussiness intelligence solutions for service provider networks. Our results are applicable to any mobile platform of iOS, android and windows.
2020-07-06
Ben, Yongming, Han, Yanni, Cai, Ning, An, Wei, Xu, Zhen.  2019.  An Online System Dependency Graph Anomaly Detection based on Extended Weisfeiler-Lehman Kernel. MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM). :1–6.
Modern operating systems are typical multitasking systems: Running multiple tasks at the same time. Therefore, a large number of system calls belonging to different processes are invoked at the same time. By associating these invocations, one can construct the system dependency graph. In rapidly evolving system dependency graphs, how to quickly find outliers is an urgent issue for intrusion detection. Clustering analysis based on graph similarity will help solve this problem. In this paper, an extended Weisfeiler-Lehman(WL) kernel is proposed. Firstly, an embedded vector with indefinite dimensions is constructed based on the original dependency graph. Then, the vector is compressed with Simhash to generate a fingerprint. Finally, anomaly detection based on clustering is carried out according to these fingerprints. Our scheme can achieve prominent detection with high efficiency. For validation, we choose StreamSpot, a relevant prior work, to act as benchmark, and use the same data set as it to carry out evaluations. Experiments show that our scheme can achieve the highest detection precision of 98% while maintaining a perfect recall performance. Moreover, both quantitative and visual comparisons demonstrate the outperforming clustering effect of our scheme than StreamSpot.
2020-06-03
Cedillo, Priscila, Camacho, Jessica, Campos, Karina, Bermeo, Alexandra.  2019.  A Forensics Activity Logger to Extract User Activity from Mobile Devices. 2019 Sixth International Conference on eDemocracy eGovernment (ICEDEG). :286—290.

Nowadays, mobile devices have become one of the most popular instruments used by a person on its regular life, mainly due to the importance of their applications. In that context, mobile devices store user's personal information and even more data, becoming a personal tracker for daily activities that provides important information about the user. Derived from this gathering of information, many tools are available to use on mobile devices, with the restrain that each tool only provides isolated information about a specific application or activity. Therefore, the present work proposes a tool that allows investigators to obtain a complete report and timeline of the activities that were performed on the device. This report incorporates the information provided by many sources into a unique set of data. Also, by means of an example, it is presented the operation of the solution, which shows the feasibility in the use of this tool and shows the way in which investigators have to apply the tool.

Qawasmeh, Ethar, Al-Saleh, Mohammed I., Al-Sharif, Ziad A..  2019.  Towards a Generic Approach for Memory Forensics. 2019 Sixth HCT Information Technology Trends (ITT). :094—098.

The era of information technology has, unfortunately, contributed to the tremendous rise in the number of criminal activities. However, digital artifacts can be utilized in convicting cybercriminal and exposing their activities. The digital forensics science concerns about all aspects related to cybercrimes. It seeks digital evidence by following standard methodologies to be admitted in court rooms. This paper concerns about memory forensics for the unique artifacts it holds. Memory contains information about the current state of systems and applications. Moreover, an application's data explains how a criminal has been interacting the application just before the memory is acquired. Memory forensics at the application level is currently random and cumbersome. Targeting specific applications is what forensic researchers and practitioner are currently striving to provide. This paper suggests a general solution to investigate any application. Our solution aims to utilize an application's data structures and variables' information in the investigation process. This is because an application's data has to be stored and retrieved in the means of variables. Data structures and variables' information can be generated by compilers for debugging purposes. We show that an application's information is a valuable resource to the investigator.

Amato, Giuseppe, Falchi, Fabrizio, Gennaro, Claudio, Massoli, Fabio Valerio, Passalis, Nikolaos, Tefas, Anastasios, Trivilini, Alessandro, Vairo, Claudio.  2019.  Face Verification and Recognition for Digital Forensics and Information Security. 2019 7th International Symposium on Digital Forensics and Security (ISDFS). :1—6.

In this paper, we present an extensive evaluation of face recognition and verification approaches performed by the European COST Action MULTI-modal Imaging of FOREnsic SciEnce Evidence (MULTI-FORESEE). The aim of the study is to evaluate various face recognition and verification methods, ranging from methods based on facial landmarks to state-of-the-art off-the-shelf pre-trained Convolutional Neural Networks (CNN), as well as CNN models directly trained for the task at hand. To fulfill this objective, we carefully designed and implemented a realistic data acquisition process, that corresponds to a typical face verification setup, and collected a challenging dataset to evaluate the real world performance of the aforementioned methods. Apart from verifying the effectiveness of deep learning approaches in a specific scenario, several important limitations are identified and discussed through the paper, providing valuable insight for future research directions in the field.

Khalaf, Rayan Sulaiman, Varol, Asaf.  2019.  Digital Forensics: Focusing on Image Forensics. 2019 7th International Symposium on Digital Forensics and Security (ISDFS). :1—5.

The world is continuously developing, and people's needs are increasing as well; so too are the number of thieves increasing, especially electronic thieves. For that reason, companies and individuals are always searching for experts who will protect them from thieves, and these experts are called digital investigators. Digital forensics has a number of branches and different parts, and image forensics is one of them. The budget for the images branch goes up every day in response to the need. In this paper we offer some information about images and image forensics, image components and how they are stored in digital devices and how they can be deleted and recovered. We offer general information about digital forensics, focusing on image forensics.

2020-04-17
Zollner, Stephan, Choo, Kim-Kwang Raymond, Le-Khac, Nhien-An.  2019.  An Automated Live Forensic and Postmortem Analysis Tool for Bitcoin on Windows Systems. IEEE Access. 7:158250—158263.

Bitcoin is popular not only with consumers, but also with cybercriminals (e.g., in ransomware and online extortion, and commercial online child exploitation). Given the potential of Bitcoin to be involved in a criminal investigation, the need to have an up-to-date and in-depth understanding on the forensic acquisition and analysis of Bitcoins is crucial. However, there has been limited forensic research of Bitcoin in the literature. The general focus of existing research is on postmortem analysis of specific locations (e.g. wallets on mobile devices), rather than a forensic approach that combines live data forensics and postmortem analysis to facilitate the identification, acquisition, and analysis of forensic traces relating to the use of Bitcoins on a system. Hence, the latter is the focus of this paper where we present an open source tool for live forensic and postmortem analysing automatically. Using this open source tool, we describe a list of target artifacts that can be obtained from a forensic investigation of popular Bitcoin clients and Web Wallets on different web browsers installed on Windows 7 and Windows 10 platforms.