Medical Image Tamper Detection Based on Passive Image Authentication

Abstract

Telemedicine has gained popularity in recent years. Medical images can be transferred over the Internet to enable the telediagnosis between medical staffs and to make the patient's history accessible to medical staff from anywhere. Therefore, integrity protection of the medical image is a serious concern due to the broadcast nature of the Internet. Some watermarking techniques are proposed to control the integrity of medical images. However, they require embedding of extra information (watermark) into image before transmission. It decreases visual quality of the medical image and can cause false diagnosis. The proposed method uses passive image authentication mechanism to detect the tampered regions on medical images. Structural texture information is obtained from the medical image by using local binary pattern rotation invariant (LBPROT) to make the keypoint extraction techniques more successful. Keypoints on the texture image are obtained with scale invariant feature transform (SIFT). Tampered regions are detected by the method by matching the keypoints. The method improves the keypoint-based passive image authentication mechanism (they do not detect tampering when the smooth region is used for covering an object) by using LBPROT before keypoint extraction because smooth regions also have texture information. Experimental results show that the method detects tampered regions on the medical images even if the forged image has undergone some attacks (Gaussian blurring/additive white Gaussian noise) or the forged regions are scaled/rotated before pasting.

Scalable Image Self-Embedding Based on Dual-Rate SPIHT-LDPC Reference Generation Scheme

“…Since the first part provides fundamental quality scale, assigning higher redundancy rate for this part enables the recovery system for high tampering rates. In order to manage data payload for watermarking, the allocated rates should satisfy (2).…”

“…Nowadays, by developing the internet and social networks, image authentication is a very crucial issue as a result of information explosion [1][2][3][4]. Moreover, modern image manipulation software, like Photoshop, operating in powerful computers facilitate the image forgery [5][6][7][8][9].…”

“…Passive authentication is a fundamental method for detecting counterfeit images without having any information about the original image. Thus, pixels statistics and learning based algorithms are served for detecting tampered regions [1], [2], [10]. On the other hand, in active authentication schemes, prior information of the original image are used for tamper detection [11], [12].…”

Perceptual Image Hashing Using Latent Low-Rank Representation and Uniform LBP

“…With the rapid development of multimedia information processing technology and the growing popularity of the Internet, the dissemination of digital contents such as digital images, audio and video via internet has become more and more popular. At the same time, however, the contents of digital data can easily be modified or forged without leaving any visible traces [1][2][3]. To verify the authenticity of digital images and to protect their intellectual property, perceptual image hashing (PIH) has emerged as an effective technology for image security and authentication and has attracted extensive attention [4,5].…”

A Vertex-Based 3D Authentication Algorithm Based on Spatial Subdivision

“…Despite these algorithms obtaining favorable exp re is still room for improvement. Some techniques ignore geometric operation Widely used in digital images and videos [4][5][6][7][8][9][10][11][12][13][14][15], fragile watermarking technique has been a popular research topic, and numerous studies have obtained favorable results. The research and development of 3D model protection mechanisms, which have numerous applications, have gradually received scholarly attention.…”