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This title explains both Cloud security and privacy, and digital forensics in a unique, systematical way. It discusses both security and privacy of Cloud and digital forensics in a systematic way.

•Automated diatom identification in human tissues using convolutional neural network.•Designing a methodology competitive with forensic experts in diatom quantification.•Applying a deep learning method to analyze digital whole-slide image. Diatom examinations have been widely used to perform drowning diagnosis in forensic practice. However, current methods for recognizing diatoms, which use light or electron microscopy, are time-consuming and laborious and often result in false positive or negative decisions. In this study, we demonstrated an artificial intelligence (AI)-based system to automatically identify diatoms in conjunction with a classical chemical digestion approach. By employing transfer learning and data augmentation methods, we trained convolutional neural network (CNN) models on thousands or tens of thousands of tiles from digital whole-slide images of diatom smears. The results showed that the trained model identified the regions containing diatoms in the tiles. In an independent test, where the slide samples were collected in forensic casework, the best CNN model demonstrated a performance competitive with those of 5 forensic pathologists with experience in diatom quantification. This pilot study paves the way for future intelligent diatom examinations; many efficient diatom extraction methods could be incorporated into our automated system.

Detecting DeepFake videos has become a central task in modern multimedia forensics applications. This article presents a method to detect face swapped videos when the portrayed person in the video is known. We propose using a threshold classifier based on similarity scores obtained from a Deep Convolutional Neural Network (DCNN) trained for facial recognition. We compute a set of similarity scores between faces extracted from questioned videos and reference materials of the person depicted. We use the highest score to classify the questioned videos as authentic or fake, depending on the threshold chosen. We validate our method on the Celeb-DF (v2) dataset (Li et al., 2020) [13]. Using the training and testing splits specified on the dataset, we obtained an HTER of 0.020 and an AUC of 0.994, surpassing the most robust approaches against this dataset (Tran et al., 2021) [37]. Additionally, a logistic regression model was used to convert the highest score into a likelihood ratio for greater applicability in forensic analyses. •Use of high-level cues based in biometrics characteristics for deepfake detection.•State of the art method for deepfake detection on CelebDF using face recognition scores.•A forensic evaluation method for deepfake detection using the likelihood ratio paradigm.

Autonomous vehicles (AV) are game-changing innovations that promise a safer, more convenient, and environmentally friendly mode of transportation than traditional vehicles. Therefore, understanding AV technologies and their impact on society is critical as we continue this revolutionary journey. Generally, there needs to be a detailed study available to assist a researcher in understanding AV and its challenges. This research presents a comprehensive survey encompassing various aspects of AVs, such as public adoption, driverless city planning, traffic management, environmental impact, public health, social implications, international standards, safety, and security. Furthermore, it presents emerging technologies such as artificial intelligence (AI), integration of cloud computing, and solar power usage in automated vehicles. It also presents forensics approaches, tools used, standards involved, and challenges associated with conducting digital forensics in the context of autonomous vehicles. Moreover, this research provides an overview of cyber attacks affecting autonomous vehicles, attack management, traditional security devices, threat modeling, authentication schemes, over-the-air updates, zero-trust architectures, data privacy, and the corresponding defensive strategies to mitigate such risks. It also presents international standards, guidelines, and best practices for AVs. Finally, it outlines the future directions of AVs and the challenges that must be addressed to achieve widespread adoption.

Automation is crucial for managing the increasing volume of digital evidence. However, the absence of a clear foundation comprising a definition, classification, and common terminology has led to a fragmented landscape where diverse interpretations of automation exist. This resembles the wild west: some consider keyword searches or file carving as automation while others do not. We, therefore, reviewed automation literature (in the domain of digital forensics and other domains), performed three practitioner interviews, and discussed the topic with domain experts from academia. On this basis, we propose a definition and then showcase several considerations concerning automation for digital forensics, e.g., what we classify as no/basic automation or full automation (autonomous). We conclude that it requires these foundational discussions to promote and progress the discipline through a common understanding. •Defining automation for digital forensic science.•Highlighting considerations to achieve a common understanding.•Presenting thoughts of automation from a practitioner’s perspective (3 interviews).•Summarizing existing concepts to enhance automation.

Forensic research community keeps proposing new techniques to analyze digital images and videos. However, the performance of proposed tools are usually tested on data that are far from reality in terms of resolution, source device, and processing history. Remarkably, in the latest years, portable devices became the preferred means to capture images and videos, and contents are commonly shared through social media platforms (SMPs, for example, Facebook, YouTube, etc.). These facts pose new challenges to the forensic community: for example, most modern cameras feature digital stabilization, that is proved to severely hinder the performance of video source identification technologies; moreover, the strong re-compression enforced by SMPs during upload threatens the reliability of multimedia forensic tools. On the other hand, portable devices capture both images and videos with the same sensor, opening new forensic opportunities. The goal of this paper is to propose the VISION dataset as a contribution to the development of multimedia forensics. The VISION dataset is currently composed by 34,427 images and 1914 videos, both in the native format and in their social version (Facebook, YouTube, and WhatsApp are considered), from 35 portable devices of 11 major brands. VISION can be exploited as benchmark for the exhaustive evaluation of several image and video forensic tools.

Digital transformation rapidly changes how we live our lives in the post pandemic world. Unfortunately, digital technology is not limited to law abiding organisations and citizens. Criminal organisations and individuals are quick to identify new opportunities with new technologies, and digital transformation is dramatically changing the character of crimes, terror, and other threats. The fast emergence of new crimes is facilitated by possibilities brought by disruptive technologies such as AI, Internet of Things, drones, and cryptocurrencies that can be disastrous tools in the hands of criminals. Consequently, our society needs far better capacity to prevent and investigate criminal acts to protect organisations and citizens. This brings an urgent need to proactively reform digital forensics to significantly increase our capability to meet the strain on society brought by crimes evolving in the digital transformation era. The future of forensic science is already here, characterized by a mix of opportunities and challenges. It is essential to make it harder to effectively use digital technologies for criminal activities, while leveraging the possibilities of digital technologies by those affected, law enforcement agencies, business and organisations. As digital technologies continue to evolve, we need to stay up to date with the latest developments to effectively investigate and prosecute crimes in the digital age. There is an increased reliance on digital evidence, and the amount of heterogeneous digital evidence in criminal cases keep increasing. The forensic science techniques thus become more sophisticated and play an increasingly important role. However, the scientific area is extremely broad, and beyond the capability of most forensic science labs to keep up with the technology forefront development speed. Besides an urgent need to bring up the subject to the political arena, examples of how we can meet the challenges are discussed such as by extending our cooperation, encourage and facilitate cooperation for training and education to handle the extremely broad and rapid development, working out methods for explaining and visualising evidence for the treatment and legal values of digital evidence in prosecution, and cooperation between product developers and crime investigators for swift innovation of digital forensics tools and methodologies for quickly emerging threats. This paper will highlight specific examples where modern digital techniques are used to solve crimes in the physical world as well as crimes committed in the digital domain and discuss how “good AI” can be used to fight “evil AI” and finally touch on the sensitive balance between the increased power of the new digital forensic tools and private integrity. Digital transformation is not limited to law abiding organisations and citizens, dramatically changing the character of crimes, terrorism and other threats. The fast emergence of new crimes is facilitated by possibilities brought by disruptive technologies that can be disastrous tools in the hands of criminals. This brings an urgent need to proactively reform digital forensics to meet the strain on society brought by crimes evolving in the digital transformation era. As digital technologies continue to evolve, we need to stay up to date to effectively investigate and prosecute crimes. As the scientific area is extremely broad, and beyond the capability of most forensic science labs to keep up with the technology forefront development speed we need to encourage and facilitate broad cooperation to better handle the rapid development. This paper will highlight the impact of digitalisation in digital forensics, including AI as disruptive technology, with a few examples on how we can combat “evil AI” by using “good AI”. [Display omitted] The Digital Forensic Loop. •The evolving landscape of digital forensics and the intersection of digital forensics and the rapid digitalisation.•The impact of digitalisation in digital forensics, including AI as disruptive technology, with a few examples on how we can combat “evil AI” by using “good AI”.•Digital Forensics Sweden DFS - An example of a national collaborative network on Digital Forensics.•Use of Digital Twins in forensic investigations.

Malware, a lethal weapon of cyber attackers, is becoming increasingly sophisticated, with rapid deployment and self-propagation. In addition, modern malware is one of the most devastating forms of cybercrime, as it can avoid detection, make digital forensics investigation in near real-time impossible, and the impact of advanced evasion strategies can be severe and far-reaching. This makes it necessary to detect it in a timely and autonomous manner for effective analysis. This work proposes a new systematic approach to identifying modern malware using dynamic deep learning-based methods combined with heuristic approaches to classify and detect five modern malware families: adware, Radware, rootkit, SMS malware, and ransomware. Our symmetry investigation in artificial intelligence and cybersecurity analytics will enhance malware detection, analysis, and mitigation abilities to provide resilient cyber systems against cyber threats. We validated our approach using a dataset that specifically contains recent malicious software to demonstrate that the model achieves its goals and responds to real-world requirements in terms of effectiveness and efficiency. The experimental results indicate that the combination of behavior-based deep learning and heuristic-based approaches for malware detection and classification outperforms the use of static deep learning methods.

The proliferation of cybercriminal activities from 2023 to 2025 has highlighted the critical role of digital forensics in legal proceedings; however, resource constraints often limit access to effective investigative capabilities. Despite the technical adequacy of open-source digital forensic tools, courts typically favor commercially validated solutions because of the absence of standardized validation frameworks for open-source alternatives, creating unnecessary financial barriers to high-quality forensic investigations. This study aims to validate and enhance the conceptual open-source digital forensic framework developed by Ismail et al. (2024) to ensure the legal admissibility of evidence acquired through open-source tools. Through a rigorous experimental methodology utilizing controlled testing environments, we conducted comparative analyses between commercial tools (FTK and Forensic MagiCube) and open-source alternatives (Autopsy and ProDiscover Basic) across three distinct test scenarios: preservation and collection of original data, recovery of deleted files through data carving, and targeted artifact searching. Each experiment was performed in triplicate to establish repeatability metrics, with error rates calculated by comparing the acquired artifacts with control references. Our findings demonstrate that properly validated open-source tools consistently produce reliable and repeatable results with verifiable integrity comparable to their commercial counterparts. The enhanced three-phase framework integrating basic forensic processes, result validation, and digital forensic readiness to satisfy Daubert Standard requirements while providing practitioners with a methodologically sound approach. This study contributes significantly to digital forensics by democratizing access to forensically sound investigative capabilities without compromising legal admissibility requirements, ultimately benefiting resource-constrained organizations while maintaining the evidentiary standards necessary for judicial acceptance.

The numerical description of skeletal morphology enables forensic anthropologists to conduct objective, reproducible, and structured tests, with the added capability of verifying morphoscopic-based analyses. One technique that permits comprehensive quantification of outline shape is elliptical Fourier analysis. This curve fitting technique allows a form’s outline to be approximated via the sum of multiple sine and cosine waves, permitting the profile perimeter of an object to be described in a dense (continuous) manner at a user-defined level of precision. A large amount of shape information (the entire perimeter) can thereby be collected in contrast to other methods relying on sparsely located landmarks where information falling in between the landmarks fails to be acquired. First published in 1982, elliptical Fourier analysis employment in forensic anthropology from 2000 onwards reflects a slow uptake despite large computing power that makes its calculations easy to conduct. Without hurdles arising from calculation speed or quantity, the slow uptake may partly reside with the underlying mathematics that on first glance is extensive and potentially intimidating. In this paper, we aim to bridge this gap by pictorially illustrating how elliptical Fourier harmonics work in a simple step-by-step visual fashion to facilitate universal understanding and as geared towards increased use in forensic anthropology. We additionally provide a short review of the method’s utility for osteology, a summary of past uses in forensic anthropology, and software options for calculations that largely save the user the trouble of coding customized routines.