Utilizing Machine Learning Techniques to Assess Technical Document Quality

Information is disseminated through images in newspapers, periodicals, the internet, and academic journals. With the aid of various tools such as Adobe, GIMP, and Corel Draw, distinguishing between an original image and a forgery has become increasingly challenging. Most conventional methods rely on constructed traits for detecting image counterfeiting. Image verification plays a crucial role in securing and ensuring the authenticity of individuals' identities in sensitive documents. This research proposes a machine learning approach (Support Vector Machine, SVM, and Histogram of Oriented Gradients, HOG) to identify images and confirm their authenticity. The Histogram of Oriented Gradients (HOG) is employed to extract diverse features including matching, image size, and dimensions for image verification. The training and testing phases are carried out using a Support Vector Machine (SVM). The proposed image verification technique is evaluated using extensive datasets to ascertain image recognition accuracy, alongside metrics such as specificity, sensitivity, and precision. Comparative analysis with existing techniques reveals that the average image verification accuracy of the proposed method stands at 98%, surpassing previous image verification methods.