Digital Image Forgery Detection Using Passive Techniques by Means of Keypoint Classification (original) (raw)
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Advanced Digital Image Forgery Detection by Using SIFT
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Digital Images plays an important role for transferring the information and are widely used in all areas of day-today life. Although, with the development of modern technologies, multiple software's are developed. This leads to the forgery of digital images. Copy-move Image Forgery Detection is one of the forensic techniques in which selected area of an image is get copied and then moved onto the other portion of the image. In this research, the main aim is to detect the forged region from the image. A method is proposed to detect the copymove forgery in an image, by comparing extracted key points. The SIFT (Scale Invariant Feature Transform) algorithm is used for extracting the invariant features from an image and then extract blocks by using PCA.
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International Journal of Engineering Research and Technology (IJERT), 2020
https://www.ijert.org/an-image-forgery-detection-using-sift-pca https://www.ijert.org/research/an-image-forgery-detection-using-sift-pca-IJERTV9IS060127.pdf Digital Images plays an important role for transferring the information and are widely used in all areas of day-today life. Although, with the development of modern technologies, multiple software's are developed. This leads to the forgery of digital images. Copy-move Image Forgery Detection is one of the forensic techniques in which selected area of an image is get copied and then moved onto the other portion of the image. In this research, the main aim is to detect the forged region from the image. A method is proposed to detect the copy-move forgery in an image, by comparing extracted key points. The SIFT (Scale Invariant Feature Transform) algorithm is used for extracting the invariant features from an image and then extract blocks by using PCA.
A Study on Image Forgery Detection
International Journal For Research In Applied Science & Engineering Technology, 2020
'Image Forgery' is extremely pervasive in this universe of picture altering devices, for example, photoshop. As the picture is utilized for the verification processes, this is an extreme issue. Identification of a produced picture from the first one is an incredibly extreme assignment. The unaided eye can only with significant effort distinguish the altered territory from the actual Picture. Since it is indispensable to build up a technique that can differentiate the altered picture from the actual one. "Copy-Move Forgery" is a notable class of picture forgery, in which a specific piece of the picture is replicated and afterward stuck in a similar picture to conceal some significant info. "Copy-Move Forgery's" aim either making an object "imperceptible" or makes an additional picture of an item in a predefined area. This has immense application in the field of Information Security where the protection of information is of most extreme significance. This paper helps us to explore the forged region from the actual image using various techniques, which are discussed below.
Keypoint Based Authentication And Localization Of Copy-Move Forgery In Digital Image
Malaysian Journal of Computer Science
With the development of powerful image processing tools and the increasing trend of using images as the main carrier of information, digital image forgery has become an increasingly serious issue. In copy-move forgery, one part of an image is copied and placed elsewhere in the same image. This paper puts forward an effective method based on SIFT for detecting copy-move forgery in digital image. The proposed method can accurately authenticate digital image and locate areas which have been tampered with. The algorithm starts by using scale-invariant features transform (SIFT) to extract local image features, which are known as keypoints, and then searches for similar keypoints based on their Euclidean distances. Finally, the matched keypoints, which represent the copied and pasted areas, are associated with one and another to indicate which parts of the image have been tampered with. Experiments are performed to validate the effectiveness of this method on different attacks, and to quantify its robustness against post-processing. Results show that the method is robust against several geometric processings, including JPEG compression, rotation, noise, and scaling. As a representative result, when considering the standard test dataset MICC-F220, the proposed method achieves true and false positive rates of 100% and 3.12%, respectively.
Digital Image Forgery Detection using SIFTFeature
International journal of engineering research and technology, 2018
Availability of new software’s nowadays, one can create unauthentic images quite often. Mostly used media to create unauthentic images for public sizing false reports. Also, in crime one can make changes in an image using various attacks such as copy-move attack, tampered and composite. For solving these problems of image forgery various detection methods are to be used and then compared, in order to specify which method is more beneficial to be used in a particular type of attack. To detect such modifications, a novel methodology based on SIFT: Scale Invariant Feature Transform is proposed with feature extraction which is invariant to translation, scale, noise and rotation. It comprises transformation of the input image to produce a standard results and then detection of keypoint and feature descriptor is applied along with a matching over all the keypoints. It comprises of the input image to produce standard representation and detection of duplicate image.
Survey on Keypoint Based Copy-move Forgery Detection Methods on Image
Procedia Computer Science, 2016
One of the problem in image forensics is to check the authenticity of image. This can be very important task when images are used as an evidence which cause change in judgment like, for example in a court of law. Image is forged by using different techniques but in that most common technique is copy-move forgery. Copy-move forgery is created by copying the region from a particular image and pasting that region on same image to mislead the user. This type of forgery is done using availability of new sophisticated software and applications. This type of forgery is also done in video. In this paper we survey on different keypoint based copy-move forgery detection methods with different parameters.
In this paper, we introduce a new view of Local Binary Pattern (LBP) fit with SIFT (Scale Invariant Features Transform) for robust copy-move forgery detection. This method works by computing rotation invariant subuniform local binary patterns from an image keypoints. They have performed well rotation invariance by moving into the first column the dominant bins of subuniform pattern, and circularly shifted the others bins. The image is first converted into a grayscale image, then we apply SIFT algorithm to detect scale invariant key points from the image. Subsequently, we compute the subuniform LBP and extracting the feature vector, which are matched by using Chisquare distance. Furthermore we adopt the RANSAC (Random Sample Consensus) algorithm to remove mismatches. Our experimental results reveal that the proposed method can produce accurate detection results, and it exhibits high robustness to scale and rotate forged regions.
Passive Techniques of Digital Image Forgery Detection: Developments and Challenges
Photographs and images play an important role in our life but, in this technology era, equipped with powerful, low cost and easy to use photo editing tools, people often forge photographs. This practice has posed a question mark on the trustworthiness of images. Because carefully edited and forged images are very hard to be distinguished from their genuine and original copies therefore, forgery detection and separation of the forged images from the innocent ones has become a challenging issue for image analysts. Image forgery detection procedures are generally classified into two broad categories; the active and the passive detection techniques. This paper presents a state of the art review of different passive forgery detection techniques those are proposed by different authors over time.