A Secret-Key Image Steganography Technique using Random Chain Codes

With the wide range use of digital communication technologies, the Internet has been commonly used as a channel for transmitting various images. Steganography practises have been implemented for achieving such secure transmission. The main focus of steganography is data hiding where, digital images are utilized as the cover image. One of the image steganography techniques is based on LSB method, where the secret message bits are embedded sequentially in LSB of the bytes of the carrier image. This makes the hidden message vulnerable to detection by attackers. Many secret key image steganography techniques have been developed as alternative techniques to achieve a high level of security for the hidden secret message. But, these techniques failed to use the full capacity of the carrier image. In this paper, a secret key image steganography technique has been implemented using chains of a random sequence of indices (codes) of the bytes in the carrier image. These chains have been constructed based on the secret key used. This makes the hidden message more secure and difficult to depict by attackers. Furthermore, the proposed technique uses the full capacity of the carrier image. Visual and numerical tests have been conducted for the performance of the proposed technique, the recorded results proved it can be used effectively in the field of information hiding

A Forensic Scheme for Revealing Post-processed Region Duplication Forgery in Suspected Images

Recent researches have demonstrated that local interest points alone can be employed to detect region duplication forgery in image forensics. Authentic images may be abused by copy-move tool in Adobe Photoshop to fully contained duplicated regions such as objects with high primitives such as corners and edges. Corners and edges represent the internal structure of an object in the image which makes them have a discriminating property under geometric transformations such as scale and rotation operation. They can be localised using scale-invariant features transform (SIFT) algorithm. In this paper, we provide an image forgery detection technique by using local interest points. Local interest points can be exposed by extracting adaptive non-maximal suppression (ANMS) keypoints from dividing blocks in the segmented image to detect such corners of objects. We also demonstrate that ANMS keypoints can be effectively utilised to detect blurred and scaled forged regions. The ANMS features of the image are shown to exhibit the internal structure of copy moved region. We provide a new texture descriptor called local phase quantisation (LPQ) that is robust to image blurring and also to eliminate the false positives of duplicated regions. Experimental results show that our scheme has the ability to reveal region duplication forgeries under scaling, rotation and blur manipulation of JPEG images on MICC-F220 and CASIA v2 image datasets

Image Region Duplication Forgery Detection Based on Angular Radial Partitioning and Harris Key-Points

Region duplication forgery where a part of the image itself is copied and pasted onto a different part of the same image grid is becoming more popular in image manipulation. The forgers often apply geometric transformations such as rotation and scaling operations to make the forgery imperceptible. In this study, an image region duplication forgery detection algorithm is proposed based on the angular radial partitioning and Harris key-points. Two standard databases have been used: image data manipulation and MICC-F220 (Media Integration and Communication Center– of the University of Florence) for experimentation. Experiment results demonstrate that the proposed technique can detect rotated regions in multiples of 30 degrees and can detect region duplication with different scaling factors from 0.8, to 1.2. More experimental results are presented to confirm the effectiveness of detecting region duplication that has undergone other changes, such as Gaussian noise, and JPEG compression

Region duplication forgery detection technique based on keypoint matching / Diaa Mohammed Hassan Uliyan

Manipulation of digital images is not considered a new thing nowadays. For as long as cameras have existed, photographers have been staged and images have been forged and passed off for more nefarious purposes. Region duplication is regarded as an efficient and simple operation for image forgeries, where a part of the image itself is copied and pasted into a different part of the same image grid. The detection of duplicated regions can be a challenging task in digital image forensic (DIF) when images are used as evidence to influence the judgment, such as in court of law. Existing methods have been developed in the literature to reveal duplicated regions. These methods are classified into block-based and key point-based methods. Most prior block based methods rely on exhaustive block matching on image contents and suffer from their inability to localize this type of forgery when the duplicated regions have gone through some geometric transformation operations and post-processing operations. In this research, we propose three novel approaches for detecting duplicate regions in forged images that are robust to common geometric transformations and post processing operations. In the first approach, we propose a novel method for detecting uniform and non-uniform duplicated regions with small size in forged images that is robust to geometric transformation operations such as rotation and scaling. The proposed method have adopted statistical region merging (SRM) algorithm to detect small regions, and then Harris interest points are localized in angular radial partition (ARP) of a circular region which are invariant to rotation and scale transformations. Moreover, feature vectors for a circular patch around Harris points are extracted using Hӧlder estimation regularity based descriptor (HGP-2) to reduce false positives. In the second approach, we therefore proposed a forensic algorithm to recognize the blurred duplicate regions in a synthesized forged image efficiently, especially when the forged region in the images is small. The method is based on blur metric evaluation (BME) and phase congruency (PCy). In the third approach, we proposed a detection method to reveal the forgery under illumination variations. The proposed method used Hessian to detect the keypoints and their corresponding features are represented by robust descriptor known as Center symmetric local binary pattern (CSLBP). The proposed methods be evaluated on two benchmark datasets. The first one is MICC-F220 which contains 220 JPEG images. The second dataset is an image manipulation dataset which includes 48 PNG true color. The experimental results illustrate that the proposed algorithms are robust against several geometric changes, such as JPEG compression, rotation, noise, blurring, illumination variations, and scaling. Furthermore, the proposed methods are resistant to forgery where small up to 8*8 pixels and flat regions are involved, with little visual structures. The average detection rate of our algorithm maintained 96 % true positive rate and 7 % false positive rate which outperform several current detection methods

Anti-spoofing method for fingerprint recognition using patch based deep learning machine

Today's with increasing identity theft, biometric systems based on fingerprints have a growing importance in protection and access restrictions. Malicious users violate them by presenting fabricated attempts. For example, artificial fingerprints constructed by gelatin, Play-Doh and Silicone molds may be misused for access and identity fraud by forgers to clone fingerprints. This process is called spoofing. To detect such forgeries, some existing methods using handcrafted descriptors have been implemented for assuring user presence. Most of them give low accuracy rates in recognition. The proposed method used Discriminative Restricted Boltzmann Machines to recognize fingerprints accurately against fabricated materials used for spoofing. © 2019 Karabuk Universit

State of the art in passive digital image forgery detection: copy-move image forgery

Authenticating digital images is increasingly becoming important because digital images carry important information and due to their use in different areas such as courts of law as essential pieces of evidence. Nowadays, authenticating digital images is difficult because manipulating them has become easy as a result of powerful image processing software and human knowledge. The importance and relevance of digital image forensics has attracted various researchers to establish different techniques for detection in image forensics. The core category of image forensics is passive image forgery detection. One of the most important passive forgeries that affect the originality of the image is copy-move digital image forgery, which involves copying one part of the image onto another area of the same image. Various methods have been proposed to detect copy-move forgery that uses different types of transformations. The goal of this paper is to determine which copy-move forgery detection methods are best for different image attributes such as JPEG compression, scaling, rotation. The advantages and drawbacks of each method are also highlighted. Thus, the current state-of-the-art image forgery detection techniques are discussed along with their advantages and drawbacks

Image Region Duplication Forgery Detection Based on Angular Radial Partitioning and Harris Key-Points

Region duplication forgery where a part of the image itself is copied and pasted onto a different part of the same image grid is becoming more popular in image manipulation. The forgers often apply geometric transformations such as rotation and scaling operations to make the forgery imperceptible. In this study, an image region duplication forgery detection algorithm is proposed based on the angular radial partitioning and Harris key-points. Two standard databases have been used: image data manipulation and MICC-F220 (Media Integration and Communication Center– of the University of Florence) for experimentation. Experiment results demonstrate that the proposed technique can detect rotated regions in multiples of 30 degrees and can detect region duplication with different scaling factors from 0.8, to 1.2. More experimental results are presented to confirm the effectiveness of detecting region duplication that has undergone other changes, such as Gaussian noise, and JPEG compression

A forensic scheme for revealing post-processed region duplication forgery in suspected images

Recent researches have demonstrated that local interest points alone can be employed to detect region duplication forgery in image forensics. Authentic images May be abused by copy-move tool in Adobe Photoshop to fully contained duplicated regions such as objects with high primitives such as corners and edges. Corners and edges represent the internal structure of an object in the image which makes them have a discriminating property under geometric transformations such as scale and rotation operation. They can be localised using scale-invariant features transform (SIFT) algorithm. In this paper, we provide an image forgery detection technique by using local interest points. Local interest points can be exposed by extracting adaptive non-maximal suppression (ANMS) keypoints from dividing blocks in the segmented image to detect such corners of objects. We also demonstrate that ANMS keypoints can be effectively utilised to detect blurred and scaled forged regions. The ANMS features of the image are shown to exhibit the internal structure of copy moved region. We provide a new texture descriptor called local phase quantisation (LPQ) that is robust to image blurring and also to eliminate the false positives of duplicated regions. Experimental results show that our scheme has the ability to reveal region duplication forgeries under scaling, rotation and blur manipulation of JPEG images on MICC-F220 and CASIA v2 image datasets