Evaluating Advanced Algorithms for Digital Image Tampering Detection with a Comparative Study on Accuracy Robustness and Performance

Since New, more advanced detection methods are needed to protect the validity and integrity of visual material as digital image tampering gets more complex. This work gives a thorough study, analysis, and comparison of the most up-to-date methods for finding changes in digital images. It does this by comparing their accuracy, robustness, and speed across a number of standard datasets. The paper discussed about the different methods used to change digital images, like copy-move, merging, and resampling, which make it hard for recognition systems to work. The study emphasize on a number of advanced algorithms, such as deep learning methods, as well as more standard statistical analysis tools. It goes into detail about how they work and what their basic ideas are. The suggested experimental setting is carefully thought out so that these algorithms can be tested on large datasets that include both natural and artificially altered images. In order to do a complete comparison, performance measures like recall, accuracy, F1-score, and computational speed are used. Each method is also tried to see how well it works when common image manipulations are used, such as adding noise, compressing the image, and changing its shape. The comparison results show that the algorithms were not all created equal in terms of how well they worked. Some were more accurate and reliable, while others were faster at working. Our results show that a multifaceted method is needed to find digital images that have been tampered with. They show the pros and cons of each technique. This study stresses how important it is to combine different spotting methods to make things more reliable generally. Researchers and practitioners who want to create or improve tampering detection systems will find the given thorough review very useful. This will help protect the integrity and safety of digital images as manipulation methods change.