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With the expeditious advancement in digital technology and image editing software, it has become easy to manipulate digital images. Copy-move is a common type of image forgery, which threatens the authenticity of digital images by copying and pasting a certain part of the image within the same image. The main focus of this paper is to detect single...
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... Hence, our paper proposes the latest robust hybrid image-adaptive watermarking technique to embed the watermark using a modifiable HSF making use of two different transformations. The host picture is broken up into several different blocks that are 8 × 8 [33,[62][63][64], and the blocks that have the highest entropy are the ones that are highly desired. These blocks are subjected to DWT to ascertain the existence of four sub-bands, which are denoted as LL (Low Low), LH (Low High), HL (High Low), and HH that stands for (High High). ...
A novel hybrid image-adaptive watermarking technique utilizing Discrete Wavelet Transform (DWT) and Fast Walsh Hadamard Transform (FWHT) is presented in this paper for the safety of proprietary rights. The host image is partitioned into blocks of dimension 8 × 8 for entropy calculation. DWT is used to determine the low frequency coefficients from the high entropy blocks, which are further transformed by FWHT. These transformed frequency coefficients are modeled by Gaussian distribution. The watermark is added into the standard image using a modifiable Hybrid Strength Factor (HSF), which is computed using mean, standard deviation, entropy and kurtosis of the transformed coefficients of high entropy blocks. Four HSF’s are used for embedding the watermarks with the help of limited side information consisting of locations of high entropy blocks and parameters of Gaussian distribution. Two novel contributions, firstly the amalgamation of DWT and FWHT to intensify the imperceptibility and robustness, and secondly the use of a statistical approach for watermark extraction have been presented in the paper. The efficacy of the technique is proved by better results using the performance metrics of PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity Index Measure), NCC (Normalized Cross Correlation), GMSD (Gradient Mean Structural Deviation) and UIQI (Universal Image Quality Index) There was no compromise in the security and imperceptibility of the watermark when the scheme is put through various attacks. Also, a comparison with latest approaches too proves high invisibility and robustness of the suggested technique.
... This method is limited to images only. Prakash et al. (2019) and Kaur et al. (2022) method combines SIFT and accelerated KAZE (AKAZE) features. AKZAE and SIFT extract many key points to detect altered areas, regardless of smooth areas, effectively. ...
The research community faces challenges for video forgery detection techniques as advancements in multimedia technology have made it easy to alter the original video content and share it on electronic and social media with false propaganda. The copy-move attack is the most commonly practiced type of attack in videos/images, where an object is copied and moved into the current frame or any other frame of the video. Hence an illusion of recreation can be created to forge the content. It is very difficult to differentiate to uncover the forgery traces by the naked eye. Hence, a passive method-based algorithm is proposed to scientifically investigate the statistical properties of the video by normalizing the median difference of the frames at the pixel level, and graphical analysis successfully shows the clear peak in the forged region. After that, a new deep learning approach, "You Only Look at Once", the latest eighth version of YOLO, is tuned and trained for the localization of forged objects in the real-time domain. The validation and testing results obtained from the trained YOLO V8 are successfully able to detect and localize the forged objects in the videos with mean average precision (mAP) of 0.99, recall is 0.99, precision is 0.99, and highest confidence score. The proposed YOLO V8 is fine-tuned in three different ways, and the performance of the proposed method outperforms existing state-of-the-art techniques in terms of inference speed, accuracy, precision, recall, testing, and training time.
... In the domain of CMFD, deep learning approaches have gained significant attention and have been widely utilized [23,24,25,26,27,28,29]. Cozzolino et al. [30] proposed a deep learning method that focuses on detecting image forgeries by extracting camera-specific noise patterns. ...
... For improving performance, lower values for this parameter are much preferable. FPR is the percentage of false positives that are not identified [29][30][31] . ...
p class="Keywords">Medical image analysis and categorization have seen success using artificial intelligence (AI) approaches and convolutional neural networks (CNNs). The diagnosis of COVID-19 based on the classification of chest X-ray images has been proposed in this research using a deep CNN architecture. Since there was no dataset of chest X-ray pictures that was sufficiently large and of high quality, it was difficult to execute a reliable and accurate CNN classification. The dataset is preprocessed utilizing several stages and procedures to build an acceptable training set for suggested CNN model to reach its optimal performance. This was carried out to address these complications, including the accessibility of a tiny, unbalanced dataset with poor photo quality. The datasets employed in this study included preprocessing processes such as medical image analysis, dataset balance, and data augmentation (DA). The simulation outcomes showed an accuracy of 99.80%, highlighting the strength of presented scheme in the specified application field. The comparative study used in the paper is conducted with a few ML algorithms that demonstrates the outperformance of the suggested scheme in comparison with other schemes in terms of various performance parameters. Additionally, two diagnostic tools, i.e., receiver operating characteristic (ROC) curve and precision-recall curve, that aid in the understanding of probabilistic forecast for binary (two-class) classification predictive modelling issues are also displayed in this article.</p
... Superpixel segmentation using linear spectral clustering (LSC) is done to improve the localization of forgeries. The authors [32] developed a robust method for CMFD. The proposed method uses the local binary pattern rotation invariant (LBP-Rot) to extract the structural texture information of the image. ...
... Finally, region and edge prediction are derived from two individual convolutional layers. In [32], the authors fused both block and keypoint methods towards CMF detection and localization. Block-and keypoint-based fusion techniques are used for feature extraction. ...
Images have greater expressive power than any other forms of documents. With the Internet, images are widespread in several applications. But the availability of efficient open-source online photo editing tools has made editing these images easy. The fake images look more appealing and original than the real image itself, which makes them indistinguishable and hence difficult to detect. The authenticity of digital images like medical reports, scan images, financial data, crime evidence, legal evidence, etc. is of high importance. Detecting the forgery of images is therefore a major research area. Image forgery is categorized as copy-move forgery, splicing, and retouching. In this work, a review of copy-move forgery is discussed along with the existing research on its detection and localization using both conventional and deep-learning mechanisms. The datasets used and challenges towards improving or developing novel algorithms are also presented.
... This section functions on the abridged illuminations and descriptions of the extremely recent articles and studies. Aspects of the research and technological advancements discussed in this article relate to the simultaneous identification of multiple copy-move forgeries, the theory of single counterparts, and their detection using a combination of techniques based on block and keypoint data [29]. This is achieved through the combination of block-centric detection techniques, an example of which is AS or the adaptive over-segmentation mechanism, as well as keypoint-based detection methods, an example of which is AKAZE or the accelerated Kaze paradigm or SIFT, which can be elaborate as the scale-invariant feature transform technique, which leverages the suggested methodology as additionally robust against different geometrical attacks while also being computationally reasonable. ...
... This is achieved through the combination of block-centric detection techniques, an example of which is AS or the adaptive over-segmentation mechanism, as well as keypoint-based detection methods, an example of which is AKAZE or the accelerated Kaze paradigm or SIFT, which can be elaborate as the scale-invariant feature transform technique, which leverages the suggested methodology as additionally robust against different geometrical attacks while also being computationally reasonable. Furthermore, the input picture is transformed into the distinctive color channel entities, and the 'Cr' channel is then utilized for further computing as it notices tampering based artifacts that are not perceptible to the naked eye in the original image [29]. ...
... Furthermore, because a suitable number of key point entities are extracted, Even in smooth areas, the copy move forgery may be identified with greater precision, resulting in more accurate detection of the forgery. On the COVERAGE, MICC-F220, GRIP, and the IMD datasets, the experimental results are carried out, and various performance based measurement criteria and metrics, such as the precision metric and the recall metric, F1 and F2 metrics, are measured and evaluated [29]. The observed derivatives demonstrate that the proposed technique in the aforementioned paper is resistant to a variety of geometrical attacks, including rotation, scaling, JPEG compression, and noise addition, and that it performs significantly better than the other existing techniques when compared to them. ...
Copy move forgeries are a type of digital retouching that takes place in the field of image modification. This type of retouching occurs when one element of a picture is replicated over to a different segment of the photograph. This is typically done in an effort to mask or cover up undesirable qualities of the photograph thus copy move forgeries are a form of image modification. When it comes to digital picture modalities, copy move forging is by far the most prevalent form of picture manipulation that may take place. This is mainly observed when certain elements or segments of the recorded file are duplicated in the recorded file. The identification and localization of forgeries is a prominent problem that has piqued the attention of scholars working in the field of digital forensics and continues to do so. The identification of photo forgery has previously been the subject of several methodologies and uncountable publications, all of which were created and published. An abridged description of the present methodology and approaches is provided in this document, along with a survey and condensed summaries of current research and successes, among other things.
Image copy-move forgery detection (CMFD) has become a challenging problem due to increasingly powerful editing software that makes forged images increasingly realistic. Existing algorithms that directly connect multiple scales of features in the encoder part may not effectively aggregate contextual information, resulting in poor performance. In this paper, an end-to-end context multiscale cross-fusion network (CMCF-Net) is proposed to detect image copy-move forgery. The proposed network consists of a multiscale feature extraction fusion (MSF) module and a multi-information fusion decoding (MFD) module. Multiscale information is efficiently extracted and fused in the MSF module utilizing stacked-scale feature fusion, which improves the network's forgery localization ability on objects of different scales. The MFD module employs contextual information combination and weighted fusion of multiscale information to guide the network in obtaining relevant clues from correlated information at multiple different scales. Experimental results and analysis have demonstrated that the proposed CMCF-Net achieves the best localization results with higher robustness.
Manipulation in the content of digital images becoming effortless and popular due to freely availability of advance photo editing tools. Content of digital images can be manipulated by using simple steps. Photos are used in many important areas, such as the courtroom, healthcare, magazines, newspapers, etc. The image authenticity and integrity are important. In photo manipulation, copy-move is a very common photo forgery technique. It has a simple process to create forged images. Copy specific content of a photo and paste it into the same photo. We proposed an experimental approach for identification of copy-move photo manipulation using a wavelet-based SIFT (scale invariant feature transform) feature transform. DWT2 db1 is used for segmentation of forge image. The scale invariant feature transform is applied to the segmented image for feature extraction. The extracted features are matched with features of ground truth image. Finally, the experimental setup detected the copy move forgery. The experimental results are shown in terms of average accuracy 97.82%, precision 98.44%, recall 100%, and f-measure 99.21%. The proposed method performs better on the forged image suffering from various geometric attacks like translation, scaling, distortion, and combination.
