Forensic Sciences
[Abbr: Acd. Jr. AJFSc]A Comprehensive Review of Deepfake Audio Detection: Techniques, Applications, and Countermeasures
by Dharmistha Parmar, Dr. G.D. Jadav, Bhumit Chavda
Modern deepfake speech detection technologies have become very advanced, making it increasingly difficult to distinguish between genuine and synthetic audio signals. This paper sightsees the contemporary methods for generating deepfake audio detection methods, including mainly three approaches, especially text-to-speech synthesis, voice cloning, and advanced neural networks (ANN) which implement the Generative Adversarial Networks (GANs), WaveNet, and Tacotron. This paper insight into the different significances of deepfake speech in various fields, which highlights the potential applications and safekeeping risks at several levels, such as forged news propagation alongside identity theft, identity fraud, and voice phishing. The study evaluates the approaches that currently exist together with detection systems which feature, convolutional and recurrent neural networks (CNNs and RNNs), spectral analysis, and machine learning-based classifiers. There are many recent advancements in the field of deepfake detection which faces many challenges due to the increasingly sophisticated synthetic speech models. Forthcoming research must focus on improving the accuracy level of detection while developing real-time identification systems is also become an important task in the voice analysis field, and establishing the ethical guidelines to mitigate potential misuse of tools. This paper provides insights into the evolving landscape of deepfake speech detection, emphasizing the need for robust countermeasures and interdisciplinary collaboration.
Impact of Cyberbullying Pertaining to Health Amongst Male and Female Secondary School Students in Mumbai.
by Ms. Bhagyashree Kulkarni, Dr. V. S. Padhye, Ms. Mohini Kumari Singh
A significant challenge in today’s times that remains majorly in urban settings associated with extensive web access is of cyberbullying. The current research paper studies the gender differences on the impact of cyberbullying pertaining to general health amongst secondary school students in Mumbai. The sample comprised of 1622 students (Male= 823 Female=801) from 10 schools in the city. Cyberbullying scale by Stewart & John Young was used to measure cyberbullying and General Health Questionnaire by D. P. Goldberg and V. F. Hillier was used for this purpose. No significant difference was observed amongst male (M=10.15, SD=8.64) and female (M=9.53, SD=7.68) students, t (1622) = 1.54, p>.05 Findings suggest that that there is no significant difference when it comes to experiencing cyberbullying in the urban settings, as both genders are equally vulnerable as well as aware of the ramifications and physical and mental health issues associated. These findings facilitate exploring the psychological and social dynamics interplay in the experience of cyberbullying in Mumbai providing insight for tailored actions for forestalling and salubrity.
Gait Pattern Analysis in Blurred CCTV Footage: Enhancing Individual Identification Using AI and Computer Vision Techniques
by Mullai Malar K, Mrs. Kajal Vinayakrao Waghmare, Krushna Sharad Sonawane
In situations where traditional identification techniques, such as facial recognition, frequently fall short, this study investigates the use of gait pattern analysis as a reliable forensic technique for identifying individuals from low-resolution or blurry CCTV footage. To improve footage quality and extract useful gait data, the study incorporates artificial intelligence (AI)-based video augmentation using technologies such as Pixelcut AI and deep learning models. Gait characteristics, including joint angles and motion trajectories, were retrieved and examined using pose estimation tools such as Kinovea. For precise individual identification, a CNN-LSTM hybrid model was created to categorize and match gait patterns. Real-world grocery surveillance film and public gait datasets were used to validate the methodology, guaranteeing data anonymization and ethical compliance. The results confirmed the feasibility of AI-enhanced gait analysis in forensic situations, with a 91.3% accuracy in identifying persons. Concerns about legal admissibility, dataset constraints, and computational complexity are among the difficulties that are highlighted in the study. Even in situations where video is obscured or damaged, the study demonstrates that gait analysis is a reliable, consistent, and non-intrusive biometric method. It promises future opportunities for multi-biometric systems, real-time surveillance integration, and the creation of extensive, varied gait datasets. It also provides a potent substitute for facial recognition in forensic investigations.
Beyond the Scalpel: The Role of Forensic Radiology in Mass Disaster Identification
by Nilanjana Roy
In cases of both natural and man-made mass disaster scenarios present profound challenges for the dignified, accurate identification of victims. Traditional means of identification may be slow, invasive, and hampered by the fragmented or commingled nature of remains. This paper highlights the critical and expanding role that forensic radiology plays as an indispensable tool in the modern DVI process. Forensic radiology, by employing modalities such as PMCT and PMMRI, offers a non-invasive, rapid, highly detailed method for documentation and analysis of human remains. The application of radiology in DVI is multifaceted. First and foremost, it is a potent tool for primary identification by comparing post-mortem radiographs against ante-mortem medical records, especially dental radiographs and unique skeletal features. It is also instrumental in disaster triage, enabling the virtual sorting and reconciliation of commingled remains. Radiology allows the documentation of identifying characteristics such as healed fractures, surgical implants, and unique anatomic variations. Beyond identification, it provides vital data for determining cause and manner of death through the visualization of traumatic injury, foreign objects, and disease pathologies while providing protection to the DVI personnel with the detection of hazardous materials embedded within the remains. In conclusion, the integration of forensic radiology into the standard DVI protocol is very important in increasing the efficiency, accuracy, and safety of the identification process. It does not only quicken victim repatriation with the creation of a permanent, objective, and detailed record, but it also maintains dignity in human identification amidst mass fatality incidents. Further development and standardization of the process are essential for the future in disaster response.
Biomarkers in Sudden Death Syndromes: Differentiating Etiologies for Targeted Prevention
by Sanskriti Rani Sharma
Sudden Death Syndrome (SDS) presents a critical challenge in diagnosis and prevention across medicine, involving various causes from cardiac arrhythmias to respiratory and neurological emergencies. The use of biomarkers is essential for post-mortem diagnosis, risk assessment in living relatives, and understanding disease mechanisms. This review thoroughly examines the range of biomarkers related to SDS. We discuss established and new cardiac biomarkers (e.g., hs-cTn, BNP) for Sudden Cardiac Death (SCD) and channelopathies. Additionally, we explore biomarkers related to Sudden Infant Death Syndrome (SIDS), such as serotonin, inflammatory markers, and genetic indicators linked to metabolic disorders. The role of biomarkers in other sudden conditions, like D-dimer in pulmonary embolism and S100B in stroke, is also covered. A key focus is on post-mortem biomarkers to assist medicolegal investigations and on the potential of multi-omics techniques (proteomics, metabolomics) to identify new signatures. Moving forward, SDS management aims to develop aetiology-specific biomarker panels that shift the focus from retrospective diagnosis to real-time risk detection, allowing for personalised prevention strategies in high-risk groups.
The Impact of Protein Biomarkers on Time Since Death Estimation Using Diverse Molecular Techniques
by Bhumit Chavda, Dr. Kapil Kumar, Dr. Saumil Merchant
The estimation of time since death (TSD) is a critical component of forensic science, aiding in criminal investigations and legal proceedings. Accurate estimation involves considering factors such as Algor Mortis, Rigor Mortis, Lividity (Livor Mortis), chemical changes, metabolic processes, RNA, DNA, protein degradation, and radiological imaging systems. This study explores the role of biomarkers, specifically proteins, in determining TSD through various analytical techniques applied to human and animal tissues. As decomposition progresses post-mortem, specific biochemical changes occur, allowing for the identification of reliable biomarkers. Certain biochemical alterations take place as post-mortem decomposition advances, making it possible to identify trustworthy biomarkers. We examine well-known techniques such as Immuno-histochemical (IHC), ATR-FTIR, Mass spectrometry, liquid chromatography, Western blotting, and enzyme-linked immunosorbent assay (ELISA), emphasizing how well they measure the composition and degradation of proteins. We show how the identification of protein biomarkers can improve the precision of PMI estimates by combining various methods. Biomarkers and protein estimation techniques are invaluable in forensic science for estimating the time since death. By concluding, we can understand the biochemical changes that occur post-mortem and by employing advanced analytical techniques, forensic scientists can provide more accurate TSD assessments, aiding investigations and legal proceedings. The different techniques were used widely in which SDS-PAGE, Gel Electrophoresis, and Western Blot were used mostly due to their precise estimation of protein level. Keywords: Protein Estimation, PMI, Biomarkers, Molecular Techniques, Time Since Death Estimation