33 IEEE Communications Magazine • April 2018 0163-6804/18/$25.00 © 2018 IEEE ABSTRACT With the invention of new communication technologies, new features and facilities are provided in a smart healthcare framework. The features and facilities aim to provide a seamless, easy-to-use, accurate, and real-time healthcare service to clients. As health is a sensitive issue, it should be taken care of with utmost security and caution. This article proposes a new medical image forgery detection system for the healthcare framework to verify that images related to health- care are not changed or altered. The system works on a noise map of an image, applies a multi-res- olution regression filter on the noise map, and feeds the output to support-vector-machine-based and extreme-learning-based classifers. The noise map is created in an edge computing resource, while the fltering and classifcation are done in a core cloud computing resource. In this way, the system works seamlessly and in real time. The bandwidth requirement of the proposed system is also reasonable. INTRODUCTION Next-generation network technologies such as ffth generation (5G), edge computing, and cloud computing have revolutionized many sectors including the healthcare sector. Recently, the healthcare sector has seen drastic improvement in terms of facilities [1]. Many new features have been added to improve people’s satisfaction. Peo- ple can now consult with doctors without visiting them, check diabetes, heartbeat, voice abnor- mality, and emotion using various sensors. While the healthcare sector is booming, several aspects need attention to make the healthcare facility more secure and private. For example, if medical data are leaked or altered, the concerned patient may face social embarrassment or let down, while other people may gain an illegal advantage. Therefore, there should be a system in a smart healthcare framework that can check whether the medical data are changed during transmission by hackers or intruders [2]. There are two types of methods to check whether the data are changed or not: intrusive and non-intrusive. In the intrusive method, some information is added to the data in such a way that it does not hamper the message in the data. The information is called a watermark. Later, if any question arises, the watermark is extract- ed from the data and matched with the orig- inal watermark. If they do not match, the data are considered to be forged or changed. In the non-intrusive method, no watermark is added to the data. Some algorithms are used to find any distortion or change in the data by analyzing any abnormal patterns. The intrusive method some- times is not feasible, because some data may not have watermarks intentionally or unintentional- ly. As the non-intrusive method does not require any watermark, any data can be verified against change or fraud. There are many non-intrusive techniques pro- posed in the literature. In this article, we focus on non-intrusive techniques to detect image forg- ery. A good review on this topic can be found in [3]. Image forgery can be done in many ways, involving one or more images. The most common image forgeries are copy-move forgery and splic- ing. In copy-move image forgery, one or some parts are copied and pasted into other parts in the same image. This type of forgery is mainly done to conceal some information in the image. In splic- ing, some parts of an image or more images are copied and pasted into another image. This type of forgery is done mostly to defame a person. In the healthcare domain, image forgery can be serious. If a mammogram is hacked, and the intruder uses the copy-move forgery to enlarge the area of cancer, the diagnosis will be wrong, and the patient will be in life-threatening trouble. If there is an image forgery detection system in a healthcare framework, it can detect the forgery before starting the diagnostic process. In the case of a forgery, the system can ask for another sam- ple from the patient. The intrusive method (e.g., embedding a watermark in the medical image) of forgery detection is not suitable in a cloud-based smart healthcare framework mainly because of two reasons: • Embedding a watermark needs extra infor- mation for transmission, which may require extra bandwidth and cause a delay in the transmission. • Embedding a watermark may decrease the visual quality of the image, which in turn afects the diagnostic process. There are some existing medical image forgery detection systems in the literature, although the number is small. Ulutas et al. proposed a forgery detection method using a rotation invariant local binary pattern (LBPROT) and a scale invariant Ahmed Ghoneim, Ghulam Muhammad, Syed Umar Amin, and Brij Gupta ADVANCES IN NEXT GENERATION NETWORKING TECHNOLOGIES FOR SMART HEALTHCARE The authors propose a new medical image forg- ery detection system for the healthcare framework to verify that images related to healthcare are not changed or altered. The system works on a noise map of an image, applies a multi-resolution regression filter on the noise map, and feeds the output to support-vec- tor-machine-based and extreme-learning-based classifiers. Ahmed Ghoneim(corresponding author) is with King Saud University and Menoufia University; Ghulam Muhammad and Syed Umar Amin are with King Saud University;Brij Gupta is with National Institute of Technology. Digital Object Identifier: 10.1109/MCOM.2018.1700817 Medical Image Forgery Detection for Smart Healthcare