Image Forgery / Tampering Detection Using Deep Learning and Cloud (original) (raw)
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Digital Image Forgery Detection Using Deep Learning Models
One of the challenges to image trust in digital and online apps, as well as on social media, is the current situation. Image forgery detection is a technique for detecting and locating fabricated components in a modified image. A sufficient amount of features is necessary for good image forgery detection, which can be achieved using a deep learning model that does not require human feature engineering other handcraft feature techniques. In this paper we used the GoogleNet deep learning model to extract picture features and the Random Forest machine learning technique to determine whether or not the image was fabricated. The proposed approach is implemented on the publicly available benchmark dataset MICC-F220 with k-fold cross validation approach to split the data set in to training and testing dataset and also compared with the state-of-the-art approaches.
Image Forgery Detection Using Deep Neural Network
IRJET, 2023
The detection of fake images is crucial to maintain the credibility of digital content, especially in the current era of digital media and social networks. Image forgery has become increasingly common and sophisticated, posing a serious threat to the authenticity and validity of digital content. This paper presents a deep learning-based approach to image forgery detection, specifically using Error Level Analysis (ELA) with Convolutional Neural Networks (CNNs) and a pre-trained VGG-16 model. The study compares the performance of the two models and provides an in-depth analysis of the results. The experiments show that the ELA-CNN model achieves a remarkable accuracy rate of 99.87% and correctly identifies 99% of invisible images, while the VGG-16 model achieves a lower accuracy rate of 97.93% and a 75.87% validation rate. The research highlights the significance of using deep learning techniques in image forgery detection and explores the implications of the findings. The paper also discusses the limitations of the study and future enhancements that could be made to improve the precision and generalization skills of image forgery detection algorithms. This research contributes to the field of image forgery detection by providing a comprehensive comparison of deep learning-based algorithms and their effectiveness in identifying fake images. The findings of this study can be utilized to develop precise and effective image forgery detection tools to maintain the integrity of digital content and mitigate the negative consequences of picture alteration.
Image forgery detection: a survey of recent deep-learning approaches
Multimedia Tools and Applications, 2022
In the last years, due to the availability and easy of use of image editing tools, a large amount of fake and altered images have been produced and spread through the media and the Web. A lot of different approaches have been proposed in order to assess the authenticity of an image and in some cases to localize the altered (forged) areas. In this paper, we conduct a survey of some of the most recent image forgery detection methods that are specifically designed upon Deep Learning (DL) techniques, focusing on commonly found copy-move and splicing attacks. DeepFake generated content is also addressed insofar as its application is aimed at images, achieving the same effect as splicing. This survey is especially timely because deep learning powered techniques appear to be the most relevant right now, since they give the best overall performances on the available benchmark datasets. We discuss the key-aspects of these methods, while also describing the datasets on which they are trained and validated. We also discuss and compare (where possible) their performance. Building upon this analysis, we conclude by addressing possible future research trends and directions, in both deep learning architectural and evaluation approaches, and dataset building for easy methods comparison.
A New Deep Learning Based Technique To Detect Copy Move Forgery In Digital Images
IRJET, 2023
Due to the advancement of photo editing software, digital image forgery detection has become an active research area in recent years. In recent studies, deep learningbased methods outperformed hand-crafted methods in image tasks such as image classification and retrieval. As a result, the proposed method introduces a novel deep learning-based forgery detection scheme. The feature vectors are extracted using the VGG16 CNN model. After obtaining the features, the similarity between the feature vectors was investigated for the detection and localization of forgery. The test result is then compared with two other methods, and the corresponding F1measures are computed.
Image Forgery Detection Using Deeplearning by Recompressing Images
Electronics
Capturing images has been increasingly popular in recent years, owing to the widespread availability of cameras. Images are essential in our daily lives because they contain a wealth of information, and it is often required to enhance images to obtain additional information. A variety of tools are available to improve image quality; nevertheless, they are also frequently used to falsify images, resulting in the spread of misinformation. This increases the severity and frequency of image forgeries, which is now a major source of concern. Numerous traditional techniques have been developed over time to detect image forgeries. In recent years, convolutional neural networks (CNNs) have received much attention, and CNN has also influenced the field of image forgery detection. However, most image forgery techniques based on CNN that exist in the literature are limited to detecting a specific type of forgery (either image splicing or copy-move). As a result, a technique capable of efficien...
Fake Image and Document Detection using Machine Learning
International Journal of Scientific Research in Science and Technology, 2020
In the recent times, the rates of cyber crimes has been increasing tremendously. It has been proven incredibly easy to create fake documents with powerful photo editing softwares. Also social media has proven to be the largest producer of fake images as well. Various malpractices have also been on surge with the help of producing digitally manipulated fake documents. Detection of such fake documents has become mandatory and essential for unveiling of the documents/images based cyber crimes. The tampered images and documents will be detected using neural network .The output of the system will distinguish original document from a digitally morphed document. The system will be implemented using Neural Networks.
2019
This paper presents our contribution in the identification and detection of Forged files and Steganographic content using Deep Neural Networks like Convolutional Neural Network and 3D-RESNET. We have used CNN in our research as CNN’s are inspired by visual cortex. In other words, they are designed to extract consequential features which are relevant in classification i.e. the ones which minimizes the loss function. In this the kernel weights are learned by Gradient Descent so as to generate the perceptive features from images fed to the network which in result supplemented to fully connected layer that performs the final classification task. In our proposed approach we mainly consider the two different tasks. Firstly, Identification of Forged Images has been carried out in which detection of altered images which includes both extension and signature has been performed. In addition to this, we have predicted the original epitome of forged file by using convolutional neural network mo...
Image forgery detection using Convolutional Neural Networks
Advances in Computational Intelligence in Materials Science
Digital forensics vital aspect of picture identity theft has drawn a lot of notice recently. In order to establish the primitive character of images, earlier studies looked at residual pattern noise, wavelet-transformed data and facts, image pixel resolution histograms, and additional characteristics of images. In an attempt to attain high-level picture illustration with the advancement of neural network-based innovations, convolutional neural networks have recently been utilized for recognizing image counterfeiting. This model suggests constructing a convolutional neural network with a structure that is distinct from previous studies in which we attempt to interpret the features derived from each layer of convolution to recognize a variety of picture manipulation using automated feature recognition. Three convolutional layers, one fully interconnected layer, and a SoftMax classifier constitute the suggested system. Our study utilizes our own data collection as the training data, wh...
Deep learning for automated forgery detection in hyperspectral document images
Journal of Electronic Imaging, 2018
Deep learning is revolutionizing the already rapidly developing field of computer vision. The convolutional neural network (CNN) is a state-of-the-art deep learning tool that learns high level features directly from a huge dataset of labeled images. In document image processing, ink analysis allows for determination of ink age and forgery and identification of pen or writer. The spectral information of inks in hyperspectral document images provides valuable information about the underlying material and thus helps in identification and discrimination of inks based on their unique spectral signatures even if they have the same color. Ink mismatch detection is a key step in document forgery detection. Although various ink mismatch detection techniques are available in the recent literature, there is a constant need for development of more accurate and effective methods to empower automated document forgery detection. A state-of-the-art deep learning method for ink mismatch detection in hyperspectral document images is proposed. The spectral responses of ink pixels are extracted from a hyperspectral document image, reshaped to a CNN-friendly image format and fed to the CNN for classification. The proposed method effectively identifies different ink types in a hyperspectral document image for forgery detection and achieves an overall accuracy of 98.2% for blue and 88% for black inks, which is the highest accuracy among the latest techniques of ink mismatch detection on the UWA Writing Ink Hyperspectral Images (WIHSI) database and differentiates between the highest number of inks mixed in unbalanced proportions in a hyperspectral document image. Furthermore, a detailed discussion on selection of appropriate CNN architecture and classification results are presented in this paper along with comparison with the former methods of ink mismatch detection. This research opens a new window for research on automated forgery detection in hyperspectral document images using deep learning.