Detecting Audio Copy-Move Forgeries on Mel Spectrograms via Hybrid Keypoint Features

Detection of audio copy-move-forgery with novel feature matching on Mel spectrogram

Today, image forensic is an emerging area which aims at authenticating the credibility of an image. Sophisticating image editing tools make it easy to forge images in different ways and one amongst them is copy-move (CM) forgery which is considered in this paper. CM forgery modifies the content of an image by copying a portion of an image and pasting it in a distinct location in the similar image. Fraudsters, in order to conceal the fraud and to deceive the human eyes, sometimes do some post-processing operations such as rotation, scaling, multiple CM, etc. The widely used block-based methods for CM forgery detection are not robust enough to affine transformation and are not invariant to scaling, rotation, and noise. So, in this work, key-point-based CM forgery detection methods based on BRISK and ORB descriptors are proposed for detecting CM forgeries in digital images. The presented methods are dependent upon blobs, detecting using DoG operator, from which BRISK and ORB features are extracted. The extracted features are matched using Hamming distance metrics to find similar key points to identify the CM regions. The work was implemented in Python and synthesized images were used in this to analyze and compare the efficacy of the presented techniques. The experimental outcomes demonstrates that the presented technique was effectual for multi-CM attacks and geometric transformations namely rotation and scaling. Though both the methods were able to detect the CM forgeries efficiently, ORB executed faster compared to BRISK.

One of the most common forms of audio forgery is copying and moving certain audible segments of audio to other locations in the same audio. The audio features of the pasted regions in such audio forgeries become very dissimilar to the audio features of the copied segments after post-processing. This dissimilarity makes detecting such tampering a major challenge. To address this problem, we propose a robust audio copy-move forgery detection method using a Decreasing Convolutional Kernel Neural Network (DCKNN), data augmentation, and digital fusion. In the proposed algorithm, Mel spectrogram and Hilbert–Huang spectrogram of the audio are extracted, and then they are fused by weighting coefficients, which are gained through extensive experiments. New spectrogram images are generated by weighted fusion, and these spectrogram images are used to train the proposed DCKNN model. The trained DCKNN can effectively detect copy-move forgery. The DCKNN model consists of a combination of four convolutional groups, each with different sensitivities to the two audio categories. We solve the problem of different sensitivities by sequentially lowering the parameters of the convolutional layers in the four convolutional groups, thus obtaining high accuracy in audio classification. The experimental results show that the proposed scheme is robust to most typical post-processing operations, including additive noise, compression, median filtering, resampling, re-quantization, and low-pass filtering, etc al. In addition, our method shows better performance in the detection of forged audio with multiple attacks. Compared to the state-of-the-art algorithms, the proposed algorithm has advantages in terms of accuracy, precision, and F1 score.

Image morphing is a common practice nowadays. To validate a digital image is considered as a perplexing task in the field of image forensic. With numerous kind of tampering been carried out on a digital image, the paper focuses on a detection of common forgery referred to as copy-move forgery or cloning, which is nearly untraceable. The paper contemplates on the color content of the forged image and employs three different methods of feature extraction to aid the detection of forgery. The experimental results show that the feature extraction methods employed detects the forged region accurately and are also effective to rotation and scaling. A performance analysis in detection of forgery for the three methods in terms precision and recall is also presented in the paper, along with a comparison with other state-of-the-art detection methods.

With the advancements in computer technology digital image tampering like copy-move forgery has become frequent. In this paper, we present a novel DCT-based technique for detecting copy-move forgery. DCT is applied to each fixed-size overlapping block of image to represent its features. The dimension of the features is reduced using truncation. Then the feature vectors are lexicographically sorted and, duplicated image blocks will be neighboring in the sorted list. Thus duplicated image blocks will be compared in the matching step. To make the method more robust, a scheme to judge whether two feature vectors are similar is imported. Simulation results show that the proposed technique is capable of detecting the duplicated regions even when an image was distorted by JPEG compression, blurring or additive white Gaussian noise.

Photographs are taken as valid evidences in various scenarios of our day to day life. Because of the developments in the field of Image Processing, altering images according to ones need is not a difficult task. Techniques of Image Forensics play its crucial role at this juncture. One of the mostly found types of image tampering is Copy-Move forgery. A copy-move forgery is performed by copying a region in an image and pasting it on another region in the same image, mostly after some form of post-processing like rotation, scaling, blurring, noise addition, JPEG compression etc. Two types of copy-move forgery detection techniques exist in literature. They are the Block based methods and Key-point based methods. Both the methods have their own advantages and limitations. This paper presents a survey on the recent developments in block based methods. As forgeries have become popular, the importance of forgery detection is much increased. Copy-move forgery, one of the most commonly used methods, copies a part of the image and pastes it into another part of the image. In this paper, we propose a detection method of copy-move forgery that localizes duplicated regions using Zernike moments. Since the magnitude of Zernike moments is algebraically invariant against rotation, the proposed method can detect a forged region even though it is rotated. Our scheme is also resilient to the intentional distortions such as additive white Gaussian noise, JPEG compression, and blurring. Experimental results demonstrate that the proposed scheme is appropriate to identify the forged region by copy-rotate-move forgery.

A popular kind of image manipulation is copymove (CM) forgery, which entails copying and pasting a section of a picture to hide or duplicate material. An essential component of digital picture forensics is the detection of such frauds. Convolutional neural networks (CNNs), one type of deep learning technique, are used to extract informative characteristics from photographs. CNNs are well-suited for image-related tasks like forgery detection because of their reputation for being able to capture intricate patterns and structures. A reptile search algorithm using a deep transfer learning-based CM forgery detection (RSADTL-CMFD) technique is presented in this research. Neural Architectural Search Network (NASNet) feature extraction in forgery detection is used in the model that is being presented. This enables the network to efficiently extract discriminative and pertinent features from the input photos. To improve we use the reptile search algorithm (RSA) for hyperparameter tuning in order to optimize the NASNet model's performance. By optimizing the network's hyperparameters, this approach helps the model perform better and quickly adjust to various forgery detection tasks. Lastly, extreme gradient boosting (XGBoost) efficiently classifies areas of the image as authentic or manipulated/forged by using the features that were retrieved from the deep learning network. Benchmark datasets are used to test the RSADTL-CMFD model's experimental result analysis. A thorough comparison study demonstrated how the RSADTL-CMFD approach produced better results than more contemporary approaches.

Audio copy-move-forgery audio is one of the most popular methods in the field of audio forensic. This type of forgery is created by copying one or more audio segments and pasting it in another position within the same audio. In this study, for detection of the audio copy-move forgery, a new method using a keypoint-based scheme on the Mel spectrogram model of audio is presented. Firstly, Mel spectrogram image is generated from the suspicious audio. Then, SURF keypoints are obtained from each RBG color channel of Mel spectrogram image. The obtained keypoints from each channel are matched via feature vectors to reveal whether the audio file is forged or original. Finally, the proposed post-processing step is applied to eliminate possible false matches. In the method, providing sufficient final matched keypoints according to the threshold value of the number of matches which is determined by experimental studies reveals that the audio file is forged. Experimental studies are carried out on publicly available the pitch-based dataset. The performance results prove that the proposed method is more robust against even under post-processing operations like noise addition, filtering operation, and compression operation.

Copy-move forgery is a type of image tampering that involves copying a portion of an image and pasting it to another part of the same image with the intention of deceiving the viewer. In recent years, many approaches have been proposed to detect copy-move forgery, including those based on local binary patterns (LBP). In this paper, we perform a comprehensive evaluation of LBP-based methods for copy-move forgery detection using a dataset of 50 digital images. We compare the performance of four LBP-based methods, namely LBP, SIFT and SURF using metrics such as accuracy, precision, recall, and F1-score. Our results show that LBP outperforms the other methods in terms of accuracy and F1-score, while SIFT has the highest precision and recall. We also investigate the effect of various parameters, such as patch size and threshold values, on the performance of LBP. Our study provides valuable insights into the strengths and weaknesses of LBP-based methods for copy-move forgery detection, which can guide future research in this area. This study evaluates the performance of Local Binary Patterns (LBP) for detecting copy-move forgery in digital images. LBP is a widely used feature extraction technique in image processing and has been applied to various computer vision tasks, including forgery detection. The comparative study involves analyzing the accuracy, precision, recall, and F1-score of LBP and other popular forgery detection techniques, including SIFT and SURF, using a dataset of 50 digital images. The results show that LBP performs better than the other techniques, achieving an accuracy of 96.6%, precision of 94.0%, recall of 100%, and F1-score of 96.9%. This study provides useful insights for researchers and practitioners in the field of forgery detection, particularly for those interested in using LBP as a feature extraction technique.

Copy-move forgery (CMF) is considered easier to detect than general forgery mechanisms, but detecting it in the presence of multiple similar but genuine scene objects (SGOs) is non-trivial. We study the efficacy of human visual perception for copy-move image forgery detection (CMFD) involving SGOs, and compare the same with machine performance. Via an eye tracking study performed with 16 users where pairs of images (one real and the other tampered) were displayed in either parallel or serial fashion, we make the following observations: (1) Forgery detection is quicker and more accurate when images are spatially aligned and presented serially, so that the tampering is conspicuous. (2) Eye fixations focus on corresponding regions of the real and tampered images, with fewer and more localized fixations noted during serial comparison. (3) A gap is noted between CMFD performance of humans and machines, with each being more sensitive to different tampering factors. Overall, results reveal the need for systematic visual comparisons to distinguish SGOs from forged objects, as well as the promise of a human-machine collaborative framework to this end.

Copy-move forgery is the image tampering procedure where a region or more regions of the image is copied and pasted into one or more areas of the same image. Detection of image copy-move forgery is essential because, images are used as the primary element of communication and security in many places. In place of law and order, used images must be original or authentic and it is necessary to ensure its authenticity. Therefore, the detection of copy-move forgery has become a prominent and active research area. This paper highlight the different block-based approaches used in copy-move forgery detection with their important results. This paper also critically discusses the advantages and limitations of each approach and enlists the different image datasets used for image forgery detection, different unsolved issues and challenges in the field of forgery detection.

As a result of the rapid progress in editing techniques, fakes and forgeries in images became easy and pervasive. Image forgery detection methods have been implemented to reveal the image rig. Copy-move forgery is a type of forgery in which a part of the image is copied to another location of the same image to hide important information or duplicate certain objects in the original image, which makes the viewer suffer from difficulties to detect the tampered region. In this type of image forgery, it is easy to perform forgery, but more difficult to detect it, because the features on the copied parts are similar to those of other parts of the image. This paper presents two approaches for forgery detection: one based on discrete transforms and the other based on Speeded-UP Robust Feature (SURF) transform. In the first framework, a comparison is presented between different trigonometric transforms in 1D and 2D for the objective of forgery detection. This comparison study is based on the completeness rate and the time of processing for the detection. This comparison gives a conclusion that the DFT in 1D or 2D implementation is the best choice to detect copy-move forgery compared to other trigonometric transforms. For the SURF-based framework, the image is divided into blocks with 50% overlapping. SURF features are extracted for each block and the complementary image to this block. A matching process is performed on the SURF keypoints of the block and the complementary image. The number of matched keypoints between each block of interest and its complementary image is recorded. The whole image is treated on a block-by-block basis yielding 49 matching scores in a distinctive feature vector. The correlation matrix for this feature vector is created and decomposed with Singular Value Decomposition (SVD) to give singular values used to classify the whole image as being tampered or not. Different types of classifiers have been used and compared. Accuracy levels up to 100% have been recorded.

Image forgery detection is the basic key to solve many problems, especially social problems such as those in Facebook, and court cases. The common form of image forgery is the copy-move forgery, in which a section of the image is copied and pasted in another location within the same image. In this type of image forgery, it is easy to perform forgery, but more difficult to detect it, because the features of the copied parts are similar to those of the other parts of the image. This paper presents an approach for copy-move forgery detection based on block processing and feature extraction from the transforms of the blocks. In addition, a Convolutional Neural Network (CNN) is used for forgery detection. The feature extraction is implemented with serial pairs of convolution and pooling layers, and then classification between the original and tampered images is performed with and without transforms. A comparison study between different trigonometric transforms in 1D and 2D is presented for detecting the tampered parts in the image. This comparison study is based on the completeness rate for the detection. This comparison ensures that the DFT in 1D or 2D implementations is the best choice to detect copy-move forgery compared to other trigonometric transforms. In addition, the paper presents a comparison study between ten cases using the CNN learning technique to detect the manipulated image. The basic idea is to use a CNN to detect and extract features. The proposed CNN approach can also be used for active forgery detection because of its robustness to detect the manipulation of digital watermarked images or images with signatures.

A robust copy-move forgery detection technique based on discrete cosine transform and cellular automata

Since digital images are one of the most important carriers of information, their authenticity is quite important. There are miscellaneous forgery techniques for manipulating digital images, and one of those is copy-move forgery. Many forgery detection techniques have been developed for detection of copy-move forgery so far. However, the main lack of these techniques is that although they can successfully detect the copied and pasted regions on a copy-move forgery image, they are not able to determine which of the detected regions is the source region and which of them is the destination region. In this study, a novel and standalone technique has been proposed for source-destination discrimination on copy-move forgery images. The proposed technique is based on machine learning and uses Support Vector Machine. Our technique can be regarded as an appendage for the classical copy-move forgery detection algorithms, which cannot make source-destination discrimination. To the best of our knowledge, the proposed technique is the first standalone technique which makes source-destination discrimination on copy-move forgeries, in the literature, and it is the only successful source-destination discrimination technique in the literature.