In recent years, the forensic landscape has undergone unprecedented development thanks to the integration of Artificial Intelligence (AI) with forensic evidence technologies. Advanced algorithms are no longer just assisting in investigations—they now play a central role in providing solid, courtroom-admissible evidence from crime scenes. These tools rely on intelligent and in-depth analysis of images, audio, text, DNA, and behavioral patterns. This article highlights the most effective AI algorithms that have proven their value in supporting criminal justice.<br /><br />⸻<br /><br />1. Convolutional Neural Networks (CNNs) – Image and Video Analysis<br /><br />Applications at Crime Scenes:<br /> • Facial recognition in surveillance footage.<br /> • Object detection (weapons, tools, blood, fingerprints) from images or video.<br /> • Automatic monitoring of suspicious activities.<br /><br />Practical Examples:<br /> • YOLO (You Only Look Once): High-accuracy object detection in images.<br /> • FaceNet and DeepFace: Identifying suspects via surveillance footage.<br /><br />Forensic Evidence:<br />Identifying a suspect in video footage with 97% accuracy using CNNs can be critical in confirming identity, location, and time.<br /><br />⸻<br /><br />2. Supervised Machine Learning Algorithms<br /><br />Most Used Algorithms:<br /> • Random Forest: Classifying physical samples and determining their origin.<br /> • Support Vector Machine (SVM): Analyzing voice prints or DNA data.<br /> • Logistic Regression: Predicting the likelihood of criminal recurrence.<br /><br />Applications at Crime Scenes:<br /> • Automatic fingerprint classification and analysis.<br /> • DNA matching with criminal databases.<br /> • Predicting potential future crime locations based on past data.<br /><br />⸻<br /><br />3. Deep Learning Algorithms for Audio and Behavior Analysis<br /><br />Examples:<br /> • Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM):<br /> • Analyzing suspects’ voice recordings.<br /> • Detecting emotions, threats, or confessions in phone calls.<br /><br />Evidential Use:<br />In kidnapping or extortion cases, LSTM can detect deception or stress in voice, serving as crucial supporting evidence for prosecutors.<br /><br />⸻<br /><br />4. Natural Language Processing (NLP) Algorithms<br /><br />Applications in Textual Evidence:<br /> • Analyzing threatening messages or emails.<br /> • Identifying authorship via stylometry techniques.<br /> • Detecting inconsistencies in witness statements.<br /><br />Role in Evidence:<br />Matching writing style between a threat document and a specific individual can be used as strong circumstantial evidence to support a case.<br /><br />⸻<br /><br />5. Clustering and Network Analysis Algorithms<br /><br />Common Algorithms & Applications:<br /> • Linking recurring crimes to a single offender.<br /> • Identifying geographical and temporal crime patterns.<br /> • Building “criminal networks” by analyzing digital and social connections between suspects.<br /><br />Forensic Use:<br />Tracing a suspect through digital or social connections using network analysis has become a powerful complementary tool, especially in organized crime investigations.<br /><br />⸻<br /><br />6. Biometric and Thermal Data Analysis Algorithms<br /><br />Applications at Crime Scenes:<br /> • Facial or iris recognition algorithms.<br /> • Thermal image analysis to estimate time of death.<br /> • Linking thermal fingerprints to a person shortly after the crime.<br /><br />Technologies Used:<br /> • Thermal Imaging AI to read invisible prints.<br /> • Gait Analysis to identify suspects based on their walk captured in video footage.<br /><br />⸻<br /><br />7. Algorithms for Detecting Forgery and Fraud<br /><br />Key Technologies:<br /> • Autoencoders and GANs: Detect digital manipulation in photos or videos.<br /> • Document Forensics Models: Identify forged or altered documents.<br /><br />Practical Examples:<br /> • Detecting tampering in surveillance footage using GAN detection tools.<br /> • Uncovering fake signatures using deep learning.<br /><br />Digital Evidence:<br />Proving tampering in a recorded video or document through AI enhances the credibility and legal strength of digital evidence, making it more resilient against legal challenges.<br /><br />⸻<br /><br />AI-powered forensic algorithms have become indispensable tools for modern criminal investigators.<br />As data processing and analytical techniques continue to evolve, these algorithms are increasingly capable of delivering scientific, precise, and admissible evidence in courtrooms. However, legal and ethical challenges remain, demanding a robust regulatory framework to ensure that technology serves justice—without replacing it.<br /><br /><br /><br />"AL_mustaqbal University is the first university in Iraq"<br/><br/><a href=https://uomus.edu.iq/Default.aspx target=_blank>al-mustaqbal University Website</a>