
The importance of digital image authentication has grown in the last decade particularly with the widespread availability of digital media and image manipulation tools. As a result, different techniques were developed to detect fraudulent alterations in digital images and restore the original data. In this paper, a new algorithm is proposed to authenticate images by hiding a copy of the approximation band in the original image. The approximation band is hidden by embedding it inside the image pixels. The intensity of the hiding was decided using a perceptual map that simulates the human vision system and adds more intensity in areas where the human eye cannot recognize changes. The perceptual map consists of three parts, luminance mask, texture mask, and edge detection mask. Results show a high ability to blindly recover images after different attacks such as removing and blocking attacks. At the same time, the structure similarity index of resultant images was higher than 0.99 for all tested images.
Artificial intelligence, Single Image Super-Resolution Techniques, Image Inpainting, Human visual system model, Resampling Detection, Image processing, Computer security, Image Forgery Detection, Image (mathematics), Digital image, Camera Model Identification, Digital Image Watermarking Techniques, QA75.5-76.95, Computer science, Enhanced Data Rates for GSM Evolution, Luminance, Image Authentication, Authentication (law), Electronic computers. Computer science, Computer Science, Physical Sciences, Computer vision, Computer Vision and Pattern Recognition, Pixel, Digital Image Forgery Detection and Identification
Artificial intelligence, Single Image Super-Resolution Techniques, Image Inpainting, Human visual system model, Resampling Detection, Image processing, Computer security, Image Forgery Detection, Image (mathematics), Digital image, Camera Model Identification, Digital Image Watermarking Techniques, QA75.5-76.95, Computer science, Enhanced Data Rates for GSM Evolution, Luminance, Image Authentication, Authentication (law), Electronic computers. Computer science, Computer Science, Physical Sciences, Computer vision, Computer Vision and Pattern Recognition, Pixel, Digital Image Forgery Detection and Identification
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
