Abstract
Privacy and security concerns regarding digital information have been increasing with advancements in multimedia and network technologies. Digital images are an integral component of online-distributed content, which plays a crucial role in forming public perceptions and opinions. They also play a major role in sensitive areas, such as law and defense. Any attempt to tamper with them is, therefore, a serious issue. One common approach to this problem is fragile image watermarking, which is used to ensure the authenticity of digital content. In this paper, a new fragile watermarking method for the authentication of digital images is proposed based on a binary rotation invariant and noise tolerant (BRINT) local texture descriptor and an extreme learning machine (ELM). BRINT is used to generate and retrieve the watermark in both the embedding and extraction procedures. In parallel, ELM is used in both procedures to learn and recover any tampered areas. The experimental results showed that the proposed scheme does not degrade image quality, allows for tamper detection, and has a recovery ability comparable with state-of-the-art fragile and semi-fragile watermarking schemes. Moreover, the proposed scheme has been validated as a fragile watermarking method with the potential to detect and locate modifications in digital images, such as copy-paste forgery, JPEG compression, and noise addition. This method is useful in sensitive fields, like defense, law, and journalism, in which decision-making based on digital visual information is necessary, to ensure that an image is authentic and has not been tampered with.