INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303
Enhanced Hashing Approach For Image Forgery Detection With Feature Level Fusion G. Mathumitha
R. Murugesan
PG Scholar, Department of CSE, Paavai College of Engineering, Namakkal, India.
Assistant Professor, Department of CSE, Paavai College of Engineering, Namakkal, India.
Abstract—Image forgery detection and its accuracy are addressed in the proposed work. The image authentication process aims at finding the originality of an image. Due to the advent of many image editing software image tampering has become common. The Enhanced hashing approach is suggested for image authentication. The concept of Hashing has been used for searching images from large databases. It can also be applied to image authentication as it produces different results with respect to the change in image. But the hashing methods used for similarity searches cannot be used for image authentication since they are no sensitive for small changes. Moreover, we need a system that detects only perceptual changes. A new hashing method, namely, enhanced robust hashing is proposed for image authentication, which uses global and local properties of an image. This method is developed for detecting image forgery, including removal, insertion, and replacement of objects, and abnormal color modification, and for locating the forged area. The local models include position and texture information of object regions in the image. The hash mechanism uses secret keys for encryption and decryption. IP tracing is done to track the suspicious nodes. Index Terms—Image forgery, image hashing, global and local properties, perceptual hashing, image authentication —————————— —————————— editing software and its widespread use, image authentication becomes important to avoid image forgery. Hashing can be efficiently used to authenticate an image since a small change in the image will produce a different hash code when the same hash function is used.
1 INTRODUCTION Digital images are increasingly transmitted over non-secure channels such as the Internet. Therefore, military, medical and quality control images must be protected against security attacks. Hence, image authentication has become a mandatory process in image sharing. An image hash function maps an image to a short binary string based on the image's appearance to the human eye. With advancement in technology, there are many multimedia data available over the internet. As storage becomes less costly, all the data are stored in database as blob objects. One primitive way for dealing with massive multimedia databases is the similarity search problem. It aims to retrieve similar objects to the query object from the database. Particularly, similarity search is at the heart of many multimedia applications, such as image retrieval, video recommendation, event detection, and face recognition. To improve the performance of similarity search, a long stream of research efforts has been made in the database community.
In general, a hash should be short, robust against simple image modifications and sensitive against major modifications. Therefore the objective is to provide a reasonably short hash code for an image with good performance. Global moments of the luminance and chrominance components are used to reflect the image’s global characteristics, and extract local texture features from salient regions in the image to represent the contents in the corresponding areas.
2 PROPOSED IMAGE AUTHENTICATION PROTOCOL
Because of the difference in dimensionality it is difficult to find the exact image using similarity search. To address this issue approximate similarity search has been implemented in recent years, which brings related images as a result instead of exact images for the given query. With the advent of many image
Many previous schemes are either based on global or local features. Global features are generally short but insensitive to changes of small areas in the image, while local features can reflect regional modifications but usually produce longer hashes. Therefore, a method that generates reasonably short hash code and better reflects the properties of an image is required. The proposed work focuses on efficient and automatic techniques to
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