<![CDATA[The increasing use of digital documents in administrative and legal activities has expanded the use of image-based signatures for authentication and verification. However, signature images are vulnerable to manipulation using image-editing software, potentially resulting in document forgery and disputes over authenticity. This study examined the use of Error Level Analysis (ELA), perceptual hashing (pHash), and the Gray Level Co-occurrence Matrix (GLCM) to detect manipulation in signature images. It also evaluated the performance of a Support Vector Machine (SVM) in classifying genuine and forged signatures within the Digital Forensic Research Workshop (DFRWS) framework. The dataset comprised 720 signature images obtained from the Starter Handwritten Signatures Dataset. The research process involved image preprocessing, feature extraction, model training, and performance evaluation using a confusion matrix, accuracy, precision, recall, and F1-score. The model achieved an accuracy of 80.56% on previously unseen test data. The developed system also produced visual analysis outputs and generated digital investigation reports based on the DFRWS framework. These results indicate that the combination of ELA, pHash, GLCM, and SVM can support a structured digital forensic process for distinguishing between genuine and forged signature images.]]>
