IRACST - International Journal of Computer Science and Information Technology & Security (IJCSITS), ISSN: 2249-9555 Vol.7, No.2, Mar-April 2017 19 Fake Image Detection Using Machine Learning Muhammed Afsal Villan Department of Computer Science and Engineering Mar Athanasius College of Engineering, Kothamangalam Kerala, India Email:afsalashyana@gmail.com Kuncheria Kuruvilla Department of Computer Science and Engineering Mar Athanasius College of Engineering, Kothamangalam Kerala, India Email:kuncheria94@gmail.com Johns Paul Department of Computer Science and Engineering Mar Athanasius College of Engineering, Kothamangalam Kerala, India Email:johnspaul007@gmail.com Prof. Eldo P Elias Department of Computer Science and Engineering Mar Athanasius College of Engineering, Kothamangalam Kerala, India Email:eldope@gmail.com Abstract—Many fake images are spreading through digital media nowadays. Detection of such fake images is inevitable for the unveiling of the image based cybercrimes. Forging images and identifying such images are promising research areas in this digital era. The tampered images are a detected using neural network which also recognizes the regions of the image that have been manipulated and reveals the segments of the original image. It can be implemented on Android platform and hence made available to common users. The compression ratio of the foreign content in a fake image is different from that of the original image and is detected using Error Level Analysis. Another feature used along with compression ratio is image metadata. Although it is possible to alter metadata content making it unreliable on its own, here it is used as a supporting parameter for error level analysis decision Keywords -- Image forensics, Metadata analysis, Error level analysis, Multilayer perception network, Deep neural networks I. INTRODUCTION In this technological era a huge number of people have become victims of image forgery. A lot of people use technology to manipulate images and use it as evidences to mislead the court. So to put an end to this, all the images that are shared through social media should be categorized as real or fake accurately. Social media is a great platform to socialize, share and spread knowledge but if caution is not exercised, it can mislead people and even cause havoc due to unintentional false propaganda. While manipulation of most of the photoshoped images is clearly evident due to pixelization & shoddy jobs by novices, some of them indeed appear genuine. Especially in the political arena, manipulated images can make or break a politician’s credibility. Current forensic techniques require an expert to analyze the credibility of an image. We implemented a system that can determine whether an image is fake or not with the help of machine learning and thereby making it available for the common public. This paper will unfold into three sections whereby first will focus on the second will focus on the Implementation details while the last part showcase the experimental result. II. THEORY A. Metadata Analysis Most image files do not just contain a picture. They also contain information (metadata) about the picture. Metadata provides information about a picture's pedigree, including the type of camera used, color space information, and application notes. Different picture formats include different types of metadata. Some formats, like BMP, PPM, and PBM contain very little information beyond the image dimensions and color space. In contrast, a JPEG from a camera usually contains a wide variety of information, including the camera's make and model, focal and aperture information, and timestamps. PNG files typically contain very little information, unless the image was converted from a JPEG or edited with Photoshop. Converted PNG files may include metadata from the source file format. Metadata provides information related to how the file was generated and handled. This information can be used to identify if the metadata appears to be from a digital camera, processed by a graphical program, or altered to convey misleading information. Common things to look for include: 1) Make, Model, and Software These identify the device or application that created the picture. Most digital cameras include a Make and Model in the EXIF metadata block. (However, the original iPhone does not!) The Software may describe the camera's firmware version or application information. 2) Image size The metadata often records the picture's dimensions. Does the rendered image size (listed at the bottom of the metadata) match the other sizes in the metadata? Many applications resize or crop pictures without updating other metadata fields.