International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023
p-ISSN: 2395-0072
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A New Deep Learning Based Technique To Detect Copy Move Forgery In Digital Images Akhila M P1, Aiswariya Raj 2 , Manju C P3 1 Electronics and Communication Engineering Federal Institute of Science And Technology Kerala, India
2Assistant Professor Electronics and Communication Engineering Federal Institute of Science And Technology
Kerala, India
3Assistant Professor Electronics and Communication Engineering Federal Institute of Science And Technology
Kerala, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Due to the advancement of photo editing
different types of forgery, the copy-move method has developed so much that it has become very difficult to find it out at a glance. The method of copy move forgery is to copy a part of the image and cunningly paste it to another part of the same image. Since the copied part of the image is pasted to the same image, so most of the image properties will be same that makes detection difficult. And the Copy move forgery detection which is a passive detection approach can be carried out without the use of PhotoShop or any other software.
software, digital image forgery detection has become an active research area in recent years. In recent studies, deep learningbased methods outperformed hand-crafted methods in image tasks such as image classification and retrieval. As a result, the proposed method introduces a novel deep learning-based forgery detection scheme. The feature vectors are extracted using the VGG16 CNN model. After obtaining the features, the similarity between the feature vectors was investigated for the detection and localization of forgery. The test result is then compared with two other methods, and the corresponding F1measures are computed.
Generally, three methods are commonly employed to detect forgery: methods that are block-based, keypointbased, or a combination of both. The block-based techniques divide the images into overlapping regular blocks and find the fit between each and every block of the entire image. Block-based techniques are more accurate but the segmentation of the image into overlapping blocks makes the approach computationally expensive. Keypoint based techniques perceive the keypoints of an image and use it to discover the copy-pasted forged region. All the above methods mentioned uses hand-crafted features. And the disadvantages of this method is that , these methods have high execution time and at low contrast these methods cannot detect forgeries. And to cope up with this problems, a new deep learning based technique to detect copy move forgery is presented .
Key Words:
Copy Move Forgery, Deep Learning, VGG16,CNN architecture, block based forgery detection
1.INTRODUCTION In today's technology environment, the Digital images are becoming a concrete information source as imaging technology progresses. They are usually seen in defence work, reporting work, medical checkups, and media work. With developments in digital image technology, such as camera equipment, programmes, and computer systems, as well as increased use of internet media, a digital image can now be considered a crucial knowledge point. Because of technical advancements and the availability of low-cost hardware and software modification equipment, as well as enhanced altering tools, picture alteration is now easier and requires less effort. Meanwhile, a wide range of picture manipulation software has put image authenticity in jeopardy. The goal of image content forgeries is to make modifications in such a way that they are difficult to detect with the naked eye, and then utilise the results for harmful purposes. So the, Photographs that have been forged are becoming more common. Without a question, image authenticity is a major worry these days. To validate the legitimacy of the modified image, there are two basic forms of image forgery detection. The first is the active technique, while the second is the passive technique.
The paper is organized as in the following manner. The related works are discussed in Section 2. Section 3 discribes the proposed method. The results of proposed method is discussed in Section 4. And the paper is concluded in Section 5.
2. RELATED WORKS Different image copy-move forgery detection techniques are considered and analyzed for the period range between (2003-2021) in this section. A recent study of copy move forgery detection mainly focus on using the SIFT algorithm. Also, most algorithms detect the copy-move forgery when the copy region did not scale and rotate. And most of the copy move forgery detection algorithms is having a very complex procedure for detecting forgery. J Fridrich et.al
Digital watermarking and digital signature are two active methods. Whereas the passive approaches include image splicing, retouching, and copy move forgery. Among
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