AUTOMATING IMAGE CAPTIONING WITH AN IMAGE AUTHENTICITY VERIFIER

Abstract

Recent visual analysis and interpretation breakthroughs are largely due to the con vergence of artificial intelligence (AI) and computer vision. Deep learning techniques have become a very effective among these methods, in particular for identifying altered photos and generating accurate captions. While the individual modules have made sig nificant progress, there is still much to learn about integrating an image captioner and an image validator into a single framework. Since proper description of visuals is the primary means of understanding visual material, this integrated approach is crucial for the visually handicapped. This type of system can provide an efficient defense against the dissemination of fake or altered photos through simultaneous tempered detection, boosting the dependability and trustworthiness. In this project, we provide a novel deep learning-based method that combines pic ture captioning and image verification. This integration produces accurate and efficient subtitles and helps determine the legitimacy of the image, regardless of whether it is tempered or not. The importance of this integration cannot be overstated for visually impaired users, it means that they may now receive accurate descriptions of the visu als in addition to being able to believe that the pictures are legitimate. By protecting against misleading information, this integrated model enhances the user’s ability to engage with and understand visual content securely and efficiently. Several standard datasets are employed to assess the system. The outcomes demon strate notable enhancements, in the reliability of the verification process and the quality of the descriptions. Based on results incorporating an image validator substantially re duces errors offering a more trustworthy solution for applications in digital asset man agement, assistive technology and automated content creation. This study addresses challenges in describing images. Makes a significant contribution to artificial intelli gence, by introducing a dual component framework. The approach minimizes. En hances the dependability of generated image descriptions through the use of an image verifier.