Search Results (501)

With the widespread use of audio editing software and artificial intelligence, it has become very easy to forge audio files. One type of these forgeries is copy-move forgery, which is achieved by copying a segment from an audio file and placing it in a different place in the same file, where the aim is to take the speech content out of its context and alter its meaning. In practice, forged recordings are often disguised through post-processing steps such as lossy compression, additive noise, or median filtering. This distorts acoustic features and makes forgery detection more difficult. This study introduces a robust keypoint-based approach that analyzes Mel-spectrograms, which are visual time-frequency representations of audio. Instead of processing the raw waveform for forgery detection, the proposed method focuses on identifying duplicate regions by extracting distinctive visual patterns from the spectrogram image. We tested this approach on two speech datasets (Arabic and Turkish) under various real-world attack conditions. Experimental results show that the method outperforms existing techniques and achieves high accuracy, precision, recall, and F1-scores. These findings highlight the potential of visual-domain analysis to increase the reliability of audio forgery detection in forensic and communication contexts. Full article

The rapid expansion of digital imagery, combined with increasingly sophisticated editing tools, has made image forgery a widespread and critical concern in fields such as journalism, forensics, and social media. This study provides a comprehensive review of Copy-Move Forgery Detection (CMFD) methods, focusing on the latest advances in deep learning-based techniques. We analyze key real-world challenges, summarize the most relevant recent solutions, and highlight persistent limitations that hinder robustness, accuracy, and practical deployment. A comparative review and qualitative analysis of prominent deep learning architectures reported in the literature is conducted to examine their relative efficiency, resilience, and trade-offs under diverse forgery scenarios. Finally, the paper highlights future research directions, including the development of more adaptable and generalizable models, the design of comprehensive benchmark datasets, the pursuit of real-time detection frameworks, and the enhancement of interpretability and transparency in CMFD systems. Full article

Background/Objectives: According to the World Drug Report 2025, cannabis is the most abused drug in the world, being sold in illicit markets in various physical forms ranging from herbal cannabis to cannabis resin and liquid cannabis. Currently, the methods used for cannabis identification are largely based on the morphological features and chemical content of the product. In this respect, identification could be severely impacted if the product is highly fragmented or pulverised. As such, DNA-based molecular techniques offer a viable alternative detection approach. In this study, we have developed a robust DNA testing method for cannabis identification, with high sensitivity and specificity. Methods/Results: Two plant DNA barcode regions, rbcL and matK, were successfully amplified in a cohort of 54 cannabis plant samples. DNA sequences obtained from these samples were blast-searched against GenBank and resulted in returned matched identity of at least 99% compared to their corresponding Cannabis sativa reference sequences. In addition, the amplification of two cannabis-unique markers, the tetrahydrocannabinolic acid synthase (THCAS) and cannabidiolic acid synthase (CBDAS) genes, produced amplicons with expected sizes only in cannabis samples; these amplicons were not detected in those plants closely related to cannabis. Sequence comparison of the majority of samples yielded at least 97% matched identity against C. sativa reference sequences in GenBank. The THCAS and CBDAS markers detected only the cannabis DNA in varying levels of cannabis–hops and cannabis–tobacco DNA mixtures. Lastly, the use of the four markers could effectively differentiate between cannabis and non-cannabis in 27 blinded samples, including 18 actual casework samples. Conclusions: In conclusion, these four genetic markers can be used to discriminate cannabis from other plant species at the genus level, especially in challenging forensic samples lacking morphological features which therefore cannot be determined by traditional detection methods. As such, this method can complement existing techniques to identify a myriad of cannabis samples. Full article

Memory forensics is an essential cybersecurity tool that comprehensively examines volatile memory to detect the malicious activity of fileless malware that can bypass disk analysis. Image-based detection techniques provide a promising solution by visualizing memory data into images to be used and analyzed by image processing tools and machine learning methods. However, the effectiveness of image-based data for detection and classification requires high computational efforts. This paper investigates the efficacy of texture-based methods in detecting and classifying memory-resident or fileless malware using different image resolutions, identifying the best feature descriptors, classifiers, and resolutions that accurately classify malware into specific families and differentiate them from benign software. Moreover, this paper uses both local and global descriptors, where local descriptors include Oriented FAST and Rotated BRIEF (ORB), Scale-Invariant Feature Transform (SIFT), and Histogram of Oriented Gradients (HOG) and global descriptors include Discrete Wavelet Transform (DWT), GIST, and Gray Level Co-occurrence Matrix (GLCM). The results indicate that as image resolution increases, most feature descriptors yield more discriminative features but require higher computational efforts in terms of time and processing resources. To address this challenge, this paper proposes a novel approach that integrates Local Interpretable Model-agnostic Explanations (LIME) with deep learning models to automatically identify and crop the most important regions of memory images. The LIME’s ROI was extracted based on ResNet50 and MobileNet models’ predictions separately, the images were resized to 128 × 128, and the sampling process was performed dynamically to speed up LIME computation. The ROIs of the images are cropped to new images with sizes of (100 × 100) in two stages: the coarse stage and the fine stage. The two generated LIME-based cropped images using ResNet50 and MobileNet are fed to the lightweight neural network to evaluate the effectiveness of the LIME-based identified regions. The results demonstrate that the LIME-based MobileNet model’s prediction improves the efficiency of the model by preserving important features with a classification accuracy of 85% on multi-class classification. Full article

Background/Objectives: SUDEP is the sudden, unexpected death of someone with epilepsy, and occurs mainly during sleep or at rest, or when the individual does not seem to have experienced a convulsive seizure. The cause of death in SUDEP is still unknown, and it may differ between cases. Cardiac factors are among the most prevalent causes observed in SUDEP. Therefore, within the forensic medicine framework, identifying well-known DNA markers involved in cardiac sudden and unexpected death would aid in understanding the cause of SUDEP, as well as in finding cardiac risk markers in patients with epilepsy. The purpose of this study was to identify any genetic variants by analyzing blood and formalin-fixed paraffin-embedded (FFPE) tissue samples, utilizing next-generation sequencing techniques. Methods: We investigated five cases of SUDEP that were examined at the Legal Medicine department of Ancona (Italy). Peripheral blood or FFPE cardiac tissues were collected, and different DNA extraction methods were performed. In particular, this study underlines a new extraction method from FFPE tissue, adapting the Casework kit for forensic application to our purpose. Later, about one hundred genes correlated to inherited cardiac diseases were sequenced through the Ion PGM System and Ion GeneStudio S5 Systems. Results: Bioinformatic analysis showed some genetic variants of unknown significance (VUS) on genes involved in SUDEP: RYR2, SCN8A, and AKAP9. Conclusions: As expected, very low coverage of the target base was observed for FFPE tissue samples because of the complexity of the biological material. Therefore, the presence of any significant variants in unamplified regions cannot be excluded in the FFPE samples. As suggested by the literature, the variants found in the blood samples are potentially associated with SUDEP. Full article

Background/Objectives: The identification of a decedent through skeletal analysis is dependent on accurate estimation of demographic characteristics, including biological sex. The most well-known sex estimation technique using the pubic bone is the Phenice method. In 2012, it was revised by Klales and colleagues and a logistic regression equation to predict sex was applied. Later, a program that estimates sex from Klales’ scoring with a random forest model, MorphoPASSE, was developed by Klales. Methods: Here we compare the accuracy of the original and revised methods, along with MorphoPASSE, using a contemporary sample of Northern Italians with documented sex. We further test the assertions by Phenice that his method is easy to employ for new observers and that ambiguity can be applied when characteristics do not morphologically fit into the categories of the method. Accuracy, error, bias, sensitivity, and specificity were calculated for each approach, along with McNemar’s tests for paired data, which compared documented sex and estimated sex. A linear weighted Cohen’s Kappa measured the differences in scoring between a new observer and an experienced observer. Results: Phenice’s method achieved higher accuracy (97%) than the Klales method and MorphoPASSE (86% each), as well as higher sensitivity and specificity, and lower error and bias. All McNemar’s tests conducted were not significant. The new observer demonstrated a similar accuracy (93%) to the experienced observer (97%). Furthermore, comparisons of Phenice’s scoring with ambiguity indicate its superior performance for capturing variation over the Klales method and MorphoPASSE. Conclusions: Phenice’s method is recommended in forensic anthropology and bioarchaeological contexts, particularly in Milan. Full article

Forensic DNA phenotyping (FDP) is an important innovation approach in forensics sciences, especially when traditional DNA profiling results are limited, mostly due to the absence of reference samples. FDP is based on the detection of genetic variants in specific genes whose function is related to pigmentation mechanisms and uses the genotypes found in the sample to determine the externally visible traits (EVT) such as the iris, hair, and skin tone or color of the individual; this prediction would help and expedite human identification processes and solve criminal cases. Several technologies have been developed to facilitate EVT prediction; however, most of them have been validated only in European populations. Implementing techniques for FDP in Latin American countries is essential given the problems of disappearance and human identification that have persisted for years. Nonetheless, scientists have a great challenge due to the admixed genetic structure of the population. This review explores the current application of FDP, emphasizing its significance, practical uses, and limitations within Latin American populations. Full article

Electrogenerated chemiluminescence (ECL) is a powerful analytical technique that combines the best features of both electrochemical and photoluminescence methods. In this work, we present a direct ECL-based method for the detection of fentanyl using unmodified screen-printed electrodes. The analysed system consists of tris(2,2′-bipyridyl)ruthenium(II) (Ru(bpy)₃²⁺) as the luminophore and fentanyl as the co-reactant. A comprehensive optimization of the experimental parameters, such as buffer pH, luminophore concentration and working electrode material, was performed in order to maximize the ECL response. The optimal conditions are identified as PBS buffer pH 6, 2.5 × 10⁻³ M Ru(bpy)₃²⁺ and bare gold screen-printed electrodes. Under these conditions, the system exhibited a strong and reproducible ECL signal, with a linear response to fentanyl concentration from 1 × 10⁻⁷ to 1 × 10⁻⁵ M and a limit of detection of 6.7 × 10⁻⁸ M. Notably, the proposed method does not require electrode surface modification, sample pretreatment or complex instrumentation, offering a rapid, sensitive, and cost-effective alternative for fentanyl detection. Furthermore, the storage of bare SPEs at room temperature in a dry place ensures their stability over months or even years, overcoming the limitations offered by ECL systems based on modifications of the working electrode with different nanomaterials. These findings highlight the potential of the proposed ECL approach as a robust and sensitive tool for the detection of synthetic opioids. Its simplicity, portability, and analytical performance make it particularly attractive for forensic and clinical applications where rapid and accurate opioid screening is essential. Full article

Diamorphine (DIM, heroin) is a semi-synthetic opioid that undergoes rapid conversion to 6-monoacetylmorphine and morphine, producing short-lived biomarkers that are difficult to capture during the process. This review critically explores the evolution of analytical techniques for quantitative DIM analysis in biological matrices from 1980 to 2025. It synthesizes findings across blood, plasma, urine, hair, sweat, and postmortem samples, emphasizing matrix-specific challenges and forensic applicability. Unlike previous opioid reviews that primarily focused on metabolites, this work highlights analytical methods capable of successfully detecting diamorphine itself alongside its key metabolites. This review examines 32 studies spanning three decades and compares three core analytical methods: gas chromatography–mass spectrometry (GC–MS), high-performance liquid chromatography (HPLC) with optical detection and liquid chromatography–mass spectrometry (LC–MS). Key performance metrics include sensitivity, sample preparation workflow, hydrolysis control, metabolite coverage, matrix compatibility, automation potential and throughput. GC–MS remains the workhorse for hair and sweat ultra-trace screening after derivatization. HPLC with UV, fluorescence or diode-array detection enables robust quantification of morphine and its glucuronides in pharmacokinetic and clinical settings. LC–MS facilitates the multiplexed analysis of DIM, its ester metabolites and its conjugates in a single, rapid run under gentle conditions to prevent ex vivo degradation. Recent advances such as high-resolution mass spectrometry and microsampling techniques offer new opportunities for sensitive and matrix-adapted analysis. By integrating validation parameters, forensic applicability, and evolving instrumentation, this review provides a practical roadmap for toxicologists and analysts navigating complex biological evidence. Full article

Background/Objectives: Forensic dental determination plays a central role in human identification, age estimation, and trauma analysis in medico-legal contexts. Traditional approaches—including clinical examination, odontometric analysis, and radiographic comparison—remain essential but are constrained by examiner subjectivity, population variability, and reduced applicability in fragmented or degraded remains. Recent advances in cone-beam computed tomography (CBCT), three-dimensional surface scanning, intraoral imaging, and artificial intelligence (AI) offer promising opportunities to enhance accuracy, reproducibility, and integration with multidisciplinary forensic evidence. The aim of this review is to synthesize conventional and emerging approaches in forensic odontology, critically evaluate their strengths and limitations, and highlight areas requiring validation. Methods: A structured literature search was performed in PubMed, Scopus, Web of Science, and Google Scholar for studies published between 2015 and 2025. Search terms combined forensic odontology, dental identification, CBCT, 3D scanning, intraoral imaging, and AI methodologies. From 108 records identified, 81 peer-reviewed articles met eligibility criteria and were included for analysis. Results: Digital methods such as CBCT, 3D scanning, and intraoral imaging demonstrated improved diagnostic consistency compared with conventional techniques. AI-driven tools—including automated age and sex estimation, bite mark analysis, and restorative pattern recognition—showed potential to enhance objectivity and efficiency, particularly in disaster victim identification. Persistent challenges include methodological heterogeneity, limited dataset diversity, ethical concerns, and issues of legal admissibility. Conclusions: Digital and AI-based approaches should complement, not replace, the expertise of forensic odontologists. Standardization, validation across diverse populations, ethical safeguards, and supportive legal frameworks are necessary to ensure global reliability and medico-legal applicability. Full article

This review explores the current landscape of neural network-based methods for digital image noise processing. Digital cameras have become ubiquitous in fields like forensics and medical diagnostics, and image noise remains a critical factor for ensuring image quality. Traditional noise suppression techniques are often limited by extensive parameter selection and inefficient handling of complex data. In contrast, neural networks, particularly convolutional neural networks, autoencoders, and generative adversarial networks, have shown significant promise for noise estimation, suppression, and analysis. These networks can handle complex noise patterns, leverage context-specific data, and adapt to evolving conditions with minimal manual intervention. This paper describes the basics of camera and image noise components and existing techniques for their evaluation. Main neural network-based methods for noise estimation are briefly presented. This paper discusses neural network application for noise suppression, classification, image source identification, and the extraction of unique camera fingerprints through photo response non-uniformity. Additionally, it highlights the challenges of generating reliable training datasets and separating image noise from photosensor noise, which remains a fundamental issue. Full article

With the rapid advancement of image processing techniques, digital image forgery detection has emerged as a critical research area in information forensics. This paper proposes a novel deep learning model based on Multi-view Multi-dimensional Forgery Detection Networks (MMFD-Net), designed to simultaneously determine whether an image has been tampered with and precisely localize the forged regions. By integrating a Multi-stream Edge Feature Learning module with a Multi-dimensional Information Fusion module, MMFD-Net employs joint supervised learning to extract semantics-agnostic forgery features, thereby enhancing both detection performance and model generalization. Extensive experiments demonstrate that MMFD-Net achieves state-of-the-art results on multiple public datasets, excelling in both pixel-level localization and image-level classification tasks, while maintaining robust performance in complex scenarios. Full article

As quantum computing advances, traditional digital forensic techniques face significant risks due to the vulnerability of classical cryptographic algorithms to quantum attacks. This review explores the emerging field of quantum digital forensics, with a particular focus on the role of quantum entanglement in enhancing the integrity, authenticity, and confidentiality of digital evidence. It compares classical and quantum forensic mechanisms, examines entanglement-based quantum key distribution (QKD), quantum hash functions, and quantum digital signatures (QDS), and discusses the challenges in practical implementation, such as scalability, hardware limitations, and legal admissibility. The paper also reviews various entanglement detection methods critical to the validation of quantum states used in forensic processes. Full article

Multimedia Forensics (MMF) investigates techniques to automatically assess the integrity of multimedia content, e.g., images, videos, or audio clips. Data-driven methodologies like Neural Networks (NNs) represent the state of the art in the field. Despite their efficacy, NNs are often considered “black boxes” due to their lack of transparency, which limits their usage in critical applications. In this work, we assess the interpretability properties of Deep High-Frequency Residuals (DHFRs), i.e., noise residuals extracted from images by NNs for forensic purposes, that nowadays represent a powerful tool for image splicing localization. Our research demonstrates that DHFRs not only serve as a visual aid in identifying manipulated regions in the image but also reveal the nature of the editing techniques applied to tamper with the sample under analysis. Through extensive experimentation on spliced amplitude Synthetic Aperture Radar (SAR) images, we establish a correlation between the appearance of the DHFRs in the tampered-with zones and their high-frequency energy content. Our findings suggest that, despite the deep learning nature of DHFRs, they possess significant interpretability properties, encouraging further exploration in other forensic applications. Full article

Gamma-hydroxybutyric acid (GHB) is a short-chain fatty acid with both endogenous and exogenous origins, complicating its detection in clinical and forensic toxicology. Due to its rapid metabolism and short detection window in conventional biological matrices, identifying reliable biomarkers of GHB exposure is crucial. This literature review aims to assess current knowledge on potential GHB biomarkers that may extend the detection window or improve specificity. A systematic search of scientific databases was conducted to identify studies investigating GHB metabolites, conjugates, and related biochemical markers using advanced analytical techniques such as LC-MS/MS and GC-MS. The review highlights promising candidates, including glycolic acid, carnitin-GHB, and glycin-GHB, as well as 3,4-dihydroxybutyric acid, which show potential for distinguishing exogenous intake. However, significant interindividual variability and limited validation studies hinder their widespread implementation. Despite promising findings, further research is needed to confirm the specificity, stability, and reproducibility of these biomarkers. This review underscores the importance of developing standardized protocols to enhance GHB exposure detection in both clinical and forensic settings. Full article