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Image forgery detection using traditional algorithms takes much time to find forgeries. The new emerging methods for the detection of image forgery use a deep neural network algorithm. A hybrid deep learning (DL) and machine learning-based approach is used in this study for passive image forgery detection. A DL algorithm classifies images into the...
Citations
... Thakur and Jindal propose a hybrid deep learning (DL) and machine learning-based approach for passive image forgery detection [19]. The DL algorithm classifies images into 2 classes: forged versus non-forged categories, whereas color illumination localizes forgery. ...
... Regression involves making predictions for continuous variables, while classification involves making decisions between two classes, typically true or false. This study employs the DCNN classification algorithm Table 1 [3][4][5][6]. The DCNN classification algorithm consists of 86,508 authentic images and 32,028 spliced images. ...
This paper uses learning-based color illumination techniques for the classification and localization of passive image geometric attack forgery classification and localization. Copy move and splicing forgery are classified using CNN. The classification accuracy obtained during validation for CASIAv1.0 is 97.35, CASIAv2.0 is 97.93, and DVMM is 97.86. The large data set is created to classify geometrical attacks by combining all the data sets with rotation and scale artifacts. The classification accuracy between rotation and scale during validation is 99.29. Machine learning-based color illumination technique is used for localization of forgery. An experiment was conducted on the CoMoFoD data set to detect passive image and geometric attack forgery. There are 48 images in the dataset with various geometric attacks such as scale and rotation. The results for identifying simple CMF attacks show an F1 score of 98.53%, a precision rate of 97.25%, and a recall rate of 100%. In the case of detecting CMF attacks on a larger scale, the F1 score is 79.1%, the precision rate is 85.2%, and the recall rate is 74.8%. For CMF attack rotation, the F1 score is 86.16%, the precision rate is 87.83%, and the recall rate is 76.33%. The proposed method demonstrates improved accuracy in detecting forgeries compared to existing approaches.
... In addition, some features like Fourier and Gabor texture features in [16] are utilized as the forensic feature to effectively study the fake sense of the image. The last one is the HOG descriptor and local binary pattern (LBP) in [37] that can effectively learn the pixel modifications in the forged images. However, these works are processed under the MICC-F220 dataset and show average performance by bearing all geometrical transformations in digitally manipulated images. ...
Image manipulation has become a common problem due to the tremendous growth of digital tools and applications. Several image forgeries can uneventfully eradicate the original contents from digital images. Copy-paste forgeries become the most challenging problem, where the image’s content is manipulated by copying and pasting from one region to another location within the same image. Existing forgery detection techniques face a high complexity in identifying the manipulated region when it is subjected to various geometrical alterations. Hence, this study introduces a novel deep learning (DL) based technique to detect the copy paste forgeries in digital images. This research undergoes three major operations pre-processing, image augmentation and classification. Image normalization, rescaling, and error level analysis (ELA) are performed in the pre-processing phase, which can improve accuracy performance. In addition, the proposed pre-processing technique can reduce the overfitting issues in the network model. Then, the image augmentation is conquered to maximize the size of the dataset images. Finally, the convolutional Autoencoder based deep learning (DL) technique is proposed to accurately classify the fake image. The MICC-F220 dataset is utilized for experimentation, and the proposed method is processed using the PYTHON platform. The proposed method achieves an overall accuracy of 99.2%, a specificity of 96.5%, a recall of 95.79% and an F1 score of 96.09%. In addition, the proposed method is compared with conventional techniques and proves the system’s efficiency.
... Our system can result in inappropriate reading due to the atmospheric conditions outside the system, like scorching weather, which may result in an inevitable rise in individual assignments [11][12][13][14]. ...
To determine the capabilities of this technology, we refer to different research papers related to this topic. This literature-based research could assist practitioners in devising responses to relevant issues and combating the COVID-19 pandemic. This paper examines the position of IoT-based technology in COVID-19. It looks at state-of-the-art architectures, networks, implementations, and industrial IoT-based solutions for combating COVID-19 in three stages: early detection, quarantine, and recovery. Since 2020 was a challenging year for all of us, and during this pandemic, we all realized that social gatherings had to be avoided, and the serious issue was to handle it. So to tackle this and ease the handling of the Corona Virus, we developed an automatic door that monitors an individual’s temperature and whether the person is wearing a mask. In the absence of a mask, it clicks a picture of the person and stores it in the database for future reference.
... However, the forensic investigation has identified the tigers termed as the "paper tigers" [3]. In the same way, in 2008, official images of four Iranian ballistic weapons were proved to be forged as one of the missiles was exposed to be counterfeited [4]. Hence, methods that could guarantee the integrity of images particularly from the evidence centred application are thus required. ...
... (RHEE, 2020) proposed utilizing texture analysis of the median filter residual of the forged image to classify the Cut-paste region in a spliced image. (Thakur & Jindal, 2020) proposed a hybrid deep learning (DL) and machine learning-based approach to detect copy move and splicing forgery. The forgery is detected using deep learning technique and the localization of the forgery is done using Machine Learning based colour illumination algorithm. ...
Nowadays, digital images have become the fastest way of transferring information, but the existence of advanced photo-editing tools makes it easy to alter digital image content which may be used as proof in a legal case, thus creating a serious problem. The most frequent techniques of image forgery include splicing and copy move. Splicing is an image forgery technique in which, the forger crops a part of the first image and places it in the second image while Copy move forgery is a type of image manipulation that involves copying and pasting at least one component of an image onto other areas of the same image for the purpose of duplication or removal of objects in the image. Finding the integrity of a digital image is critical since it can be used as a legal proof in a multitude of sectors, including investigation of a crime scene. Equally, finding the features of an image that change as a result of image manipulation such as copy move and splicing is very important as this can be used to distinguish between forged and original image. Therefore, in this paper, we looked upon the effect of image splicing and copy move forgery on haralick features of a digital image. CoMoFoD dataset and Images frames extracted from original and spliced videos were used in thisexperiment. The result of this experiment shows that splicing and copy move manipulations have no effect on haralick features of a digital image. As a result, these features cannot be used to tell if an image has been spliced or was manipulated bya copy move forgery
... (RHEE, 2020) proposed utilizing texture analysis of the median filter residual of the forged image to classify the Cut-paste region in a spliced image. (Thakur & Jindal, 2020) proposed a hybrid deep learning (DL) and machine learning-based approach to detect copy move and splicing forgery. The forgery is detected using deep learning technique and the localization of the forgery is done using Machine Learning based colour illumination algorithm. ...
Nowadays, digital images have become the fastest way of transferring information, but the existence of advanced photo-editing tools makes it easy to alter digital image content which may be used as proof in a legal case, thus creating a serious problem. The most frequent techniques of image forgery include splicing and copy move. Splicing is an image forgery technique in which, the forger crops a part of the first image and places it in the second image while Copy move forgery is a type of image manipulation that involves copying and pasting at least one component of an image onto other areas of the same image for the purpose of duplication or removal of objects in the image. Finding the integrity of a digital image is critical since it can be used as a legal proof in a multitude of sectors, including investigation of a crime scene. Equally, finding the features of an image that change as a result of image manipulation such as copy move and splicing is very important as this can be used to distinguish between forged and original image. Therefore, in this paper, we looked upon the effect of image splicing and copy move forgery on haralick features of a digital image. CoMoFoD dataset and Images frames extracted from original and spliced videos were used in this experiment. The result of this experiment shows that splicing and copy move manipulations have no effect on haralick features of a digital image. As a result, these features cannot be used to tell if an image has been spliced or was manipulated by a copy move forgery.
... The learning rate drop factor and learning rate drop period also affect the performance of the model. We achieve good accuracy as compared with the state of the art algorithms [1,5,10,26,[39][40][41]45]. All these algorithms are based on deep convolution neural network. ...
Image forgeries can be detected and localized by using deep convolution neural network, and semantic segmentation. Color illumination is used to apply color map after pre-processing step. To train VGG-16 with two classes using deep convolution neural network transfer learning approach is used. This algorithm classifies image’s pixels having a forgery or not. These classified images with color pixel label are trained using semantic segmentation to localize forged pixels. These algorithms are tested on GRIP, DVMM, CMFD, and BSDS300 datasets. All these images are divided into two folders. One folder contains all forged images, and another folder contains labels of forged pixels. The experiment result shows that total accuracy is 0.98482, average accuracy is 0.98581, average IoU is 0.91148, weighted IoU is 0.97193, and average boundary F1 score is 0.86404. The forged pixel accuracy is 0.98698, IoU of the forged pixel is 0.83945, and average boundary F1 score of the forged image is 0.79709. Not Forged pixel accuracy is 0.98463, IoU of not forged pixel is 0.98351 and average boundary F1 score of not forged image is 0.93055. The experiment results show that forged pixel and not forged detection accuracy is above 98%, which is best among other methods.
