Enhanced Hashing Approach For Image Forgery Detection With Feature Level Fusion

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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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303 received image, and compare it with the original hash value. If they match, the content is considered as authentic. In order to allow incidental distortion, the hash value must possess some robustness.

identify and verify the contents of digital images. The services provided by the proposed image authentication system are mentioned below.

1.

Identify the received image as a similar image, or a tampered image, or a different image.

2.

Evaluate similarity of two images by calculating the distance between them.

3.

Identify and locate three types of tampered area: Added area, Removed area, Changed area.

4.

Estimate the percentage of tampered area.

In [1], the authors generated image hash using Zernike Moments and local features. But the Salient region is detected as rectangular boxes which include some background details and does not clearly show the salient region. In order identify the salient region edge detection mechanism is used in the proposed work. And also IP tracing is enabled in the proposed system to find the malicious node where the image got tampered. Fig.3 explains the process steps of the proposed image authentication protocol.

The image is first rescaled to a fixed size and converted from RGB to grayscale. These steps are covered under the preprocessing step. The aim of rescaling is to ensure that the generated image hash has a fixed length and the same computation complexity. Next global and local features are extracted. Then the Global and local features are concatenated to construct a final hash value.

Received Image

Preprocessing

2.1 Edge Detection Mechanisms Our proposed work includes research on the embedding algorithm robust to geometric distortions and improving the precision in locating the altered areas by implementing via any digital multimedia networking application for verifying the content of image transmission over RGB features.

Edge detection

Hash construction

No

Match with original hash

Fake image

So this kind of implementation is desired to find features that best represent the image contents so as to enhance the hash’s sensitivity to small area tampering while maintaining short hash length, good robustness against normal image processing like edge detection mechanisms and include tracer routing to detect the content modified hacker system which is use full to reduce the hacking possibilities. So without knowledge of this method, hacker information may be acknowledged to the sender once the hacker receives the packet for content or object modifications.

Hash Function

Yes

Original Image

Fig. 2.

End Process

Here, in the final image salient features are highlighted.

Instead of rectangular boxes only the edges were traced thus giving only the salient feature. Hash can be constructed for the detected regions and transmitted along with the original image to the receiver.

IP tracing

Malicious Node Identified Fig. 1.

Salient feature identification using Edge detection.

2.2 Hashing Generation and Encryption The global and object local vectors are concatenated to form an intermediate hash, which is then pseudo-randomly scrambled based on a secret key to produce the final hash sequence.

Advanced encryption algorithms are used to encrypt the hash sequence with respect to secret keys.

Process steps for image authentication.

When an image is sent to the user, a possible solution to prove authenticity is to generate a hash value and send it securely to the user. The hash value is a compact string. It can be called as an abstract of the content. A user can regenerate hash value from the

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The user regenerates the hash value from the received image after successfully decrypting it and compares it with the original hash value. If they match, the content is considered as authentic.

Otherwise the received image is identified as a fake one.

[6]

2.3 Hamming Distance Matching [7]

Distance between hashes of an image pair is used as a metric for finding similarity or dissimilarity of the two images. The hash sequence of a received image needs to be tested with the decrypted hash sequence under similarity ratio. If the difference is above the threshold then it has been maliciously tampered else it is considered as legitimate image.

[8]

[9]

2.4 IP Tracing [10]

Tracer routing is used to find out the unauthorized router or the system that purposefully modified the content of the image and forwarded it to the destination in a routing process. This can be done by getting acknowledgment from every router in the routing process by a source. Then source compares the acknowledgement with the predefined routing table for any routing delay or IP mismatch. Thus the attacker node can be identified and reported.

[11]

[12]

[13]

3 DISCUSSION AND CONCLUSION The proposed image hashing approach is developed using both global and local features. Image hashes produced with the proposed method are robust against common image processing operations like brightness adjustment, rescaling and addition of noise. The IP tracing mechanism helps to find the malicious node thus providing a full fledged authentication mechanism.

[14]

[15]

[16] [17]

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