Image Forgery Detection Techniques Research Articles

Advance Image Forgery Detection

Abstract :- In the digital era, the proliferation of sophisticated image editing tools has made image forgery a prevalent issue, posing significant challenges in various fields, including legal proceedings, journalism, and digital security. Advanced image forgery detection techniques are crucial to ensuring the authenticity and integrity of digital images. This paper explores the state-of-the-art methods and technologies employed in the detection of image forgeries. We focus on both traditional and deep learning-based approaches, highlighting their respective strengths and weaknesses. Traditional methods, such as pixel-based, format-based, and geometry-based techniques, rely on the intrinsic properties of images to identify inconsistencies and manipulations. These methods are effective in detecting simple forgeries but often fall short against more sophisticated alterations. On the other hand, deep learning-based approaches, leveraging convolutional neural networks (CNNs) and generative adversarial networks (GANs), have shown significant promise in identifying subtle and complex forgeries by learning high-level features from large datasets. In conclusion, while significant advancements have been made in image forgery detection, continuous research and development are necessary to stay ahead of emerging forgery techniques. The integration of multidisciplinary approaches and the collaboration between academia and industry will be pivotal in advancing the efficacy of forgery detection systems. Keywords : Image Forgery Detection, Deep Learning, Digital Image Authentication, Pixel-based Methods, Image Manipulation. Image Manipulation.

Securing Medical Imaging: A Comprehensive Review and Prospective Paths for Image Authentication

Abstract: As medical image authentication systems depend more and more on cutting-edge technologies, the integrity and dependability of diagnostic procedures are seriously threatened by adversarial attacks. This abstract reviews the latest advancements in Deep Learning (DL)-specific image forgery detection techniques, highlighting common splicing and copy-move attacks as well. This survey covers a wide range of methods used in medical image forgery detection and highlights the role that sophisticated models play in maintaining the accuracy of medical images. The study looked at a range of deep learning and machine learning models as well as methods for isolating photos and using Generative Adversarial Networks (GANs) to detect tampering. The results showed that it is possible to reliably recognize intentionally created anomalies in medical imaging. The case studies show that deep learning performs exceptionally well in correctly identifying scans with injected tumours, especially when it comes to the localization of the region of interest. This works well in situations where localization is not feasible, as reduced-negativespace scans show.

Passive Image Forgery Detection Techniques: A Review, Challenges, and Future Directions

Passive Image Forgery Detection Techniques: A Review, Challenges, and Future Directions

Modeling of intelligent hyperparameter tuned deep learning based copy move image forgery detection technique

Due to the drastic increase in the generation of high-quality fake images in social networking, it is essential to design effective recognition approaches. Image/video manipulation defines any set of actions which can be carried out on digital content by the use of software editing approaches or artificial intelligence. A major kind of image and video editing comprises replicating the regions of the image, named as copy-move technique. Conventional image processing methods physically search for the pattern relevant to the replicated contents, restricting the utilization in massive classification of data. Contrastingly, the recently developed deep learning (DL) models have exhibited promising performance over the traditional models. In this aspect, this paper presents a novel intelligent deep learning based copy move image forgery detection (IDL-CMIFD) technique. The proposed IDL-CMIFD technique intends to design a DL model to classify the candidate images into two classes: original and forged/tampered and then localized the copy moved regions. In addition, the proposed IDL-CMIFD technique involves the Adam optimizer with Efficient Net based feature extractor to derive a useful set of feature vectors. Moreover, chaotic monarch butterfly optimization (CMBO) with deep wavelet neural network (DWNN) model is applied for classification purposes. The CMBO algorithm is utilized to optimally tune the parameters involved in the DWNN model in such a way that the classification performance gets improved. The performance validation of the proposed model takes place on benchmark MICC-F220, MICC-F2000, MICC-F600 datasets. A wide range of comparative analyses is performed and the results ensured the better performance of the IDL-CMIFD technique in terms of different evaluation parameters.

Image Splicing Forgery Detection using Standard Division-Local Binary Pattern Features

Numerous aspects of daily life contribute to societal stability, and the security of people's perceptions of the world online is one target of various malicious attacks. Professional forgers can now quickly create copy-move, splice, or retouch photos with the use of today's advanced tools. It has been determined that splicing, is a widespread method of manipulating images. Image forgery can also lead to substantial setbacks and challenges, some of which may have significant ethical, moral, or legal consequences. Thus, the paper proposes a system that combines SD-LBP (Standard Devision-Local Binary Pattern) based passive picture splicing detection system and ANN classifier. The SD-LBP is created to have the benefits and avoid drawbacks of Local Binary Pattern (LBP). The SD-LBP extraction is typically performed by employing proposed SD value-based thresholding instead of the center pixel, which is robust to noise and other photometric attacks. The second part of the proposed system is the ANN classifier is used that extract the feature of images to lower the error and build a model that can tell spliced images from real photos that have been digitally altered. The proposed system is creating a reliable image forgery detection technique that was implemented with CASIA V2.0 standard dataset. The results showing that it outperformed compared with other methods on the in terms of accuracy (97.8%), sensitivity (98.6%), and specificity (97.1%). Most importantly, the proposed SFD method exceeded the state-of-the-art efforts in this field in terms of accuracy.

A Thorough Investigation on Image Forgery Detection

Image forging is the alteration of a digital image to conceal some of the necessary or helpful information. It cannot be easy to distinguish the modified region from the original image in some circumstances. The demand for authenticity and the integrity of the image drive the detection of a fabricated image. There have been cases of ownership infringements or fraudulent actions by counterfeiting multimedia files, including re-sampling or copy-moving. This work presents a high-level view of the forensics of digital images and their possible detection approaches. This work presents a thorough analysis of digital image forgery detection techniques with their steps and effectiveness. These methods have identified forgery and its type and compared it with state of the art. This work will help us to find the best forgery detection technique based on the different environments. It also shows the current issues in other methods, which can help researchers find future scope for further research in this field.

Adaptive partitioning‐based copy‐move image forgery detection using optimal enabled deep neuro‐fuzzy network

AbstractThe emergence of photo editing applications, like Adobe Photoshop, has manipulated the operation of digital images into a simple task. However, these manipulations of images misrepresent the content of the original image for misleading the public. Various copy move forgery detection techniques are developed, but these show less robustness on the image with noise and blurring. This article develops an optimization‐driven deep learning technique for image forgery detection. The purpose is to develop a copy‐move image forgery detection technique using a deep neuro‐fuzzy network and a newly developed optimization algorithm. Here, adaptive partitioning is adapted using a rectangular search for splitting the image into different parts. In addition, the features like local Gabor XOR pattern and Texton features are extracted from the partition. Furthermore, the forgery is detected using the deep neuro‐fuzzy network. Finally, the deep neuro‐fuzzy network training is performed using the proposed multi‐verse invasive weed optimization (MVIWO) technique. The proposed MVIWO method will be newly designed by integrating the multi‐verse optimizer and invasive weed optimization technique. Thus, the copy‐move image forgery detection is effectively performed using the proposed MVIWO‐based deep neuro‐fuzzy network. The developed MVIWO‐based deep neuro‐fuzzy network offers superior performance with the highest specificity of 93.54%, highest accuracy of 94.01%, and highest sensitivity of 97.75%.

Image Splicing-Based Forgery Detection Using Discrete Wavelet Transform and Edge Weighted Local Binary Patterns

With the advancement of the multimedia technology, the extensive accessibility of image editing applications makes it easier to tamper the contents of digital images. Furthermore, the distribution of digital images over the open channel using information and communication technology (ICT) makes it more vulnerable to forgery. The vulnerabilities in telecommunication infrastructure open the doors for intruders to introduce deceiving changes in image data, which is hard to detect. The forged images can create severe social and legal troubles if altered with malicious purpose. Image forgery detection necessitates the development of sophisticated techniques that can efficiently detect the alterations in the digital image. Splicing forgery is commonly used to conceal the reality in images. Splicing introduces high contrast in the corners, smooth regions, and edges. We proposed a novel image forgery detection technique based on image splicing using Discrete Wavelet Transform and histograms of discriminative robust local binary patterns. First, a given color image is transformed in YCbCr color space and then Discrete Wavelet Transform (DWT) is applied on Cb and Cr components of the digital image. Texture variation in each subband of DWT is described using the dominant rotated local binary patterns (DRLBP). The DRLBP from each subband are concatenated to produce the final feature vector. Finally, a support vector machine is used to develop image forgery detection model. The performance and generalization of the proposed technique were evaluated on publicly available benchmark datasets. The proposed technique outperformed the state-of-the-art forgery detection techniques with 98.95% detection accuracy.

Image Forgery Detection and Localization

A human brain responds at a much faster rate to images and the information it contains. An image is considered as proof of past events that have occurred, but in today's world where editing tools are made available so easily tampering of images and hiding the original content has become too mainstream. The identification of these tampered images is very important as images are considered as vital sources of information in crime investigation and in various other fields. The image forgery detection techniques check the credibility of the image. Various research has been carried out in dealing with image forgery and tampering detection techniques, this paper highlights various the type of forgery and how they can be detected using various techniques. The fusion of various algorithms so that a complete reliable type of algorithm can be developed to deal mainly with copy-move and image splicing forgery. The copy-move and image splicing method are main focus of this paper.

CLIFD: A novel image forgery detection technique using digital signatures

The paper presents a new image forgery detection technique. The proposed technique uses digital signatures; it generates a digital signature for each column and embeds the signature in the least significant bits of each corresponding column’s selected pixels. The message digest algorithm 5 (MD5) is used for digital signature generation, and the fourleast-significant-bit substitution mechanism is used to embed the signature in the designated pixels. The embedding of the digital signature in the selected pixel remains completely innocent and undetectable for the human visual system. The proposed forgery detection technique has demonstrated significant results against different types of forgeries introduced to digital images and successfully detected and pointed out the forged columns.

Digital Image Forgery Detection Approaches: A Review and Analysis

Digital images have an inevitable role in almost all areas like clinical imaging, media broadcasting, crime analysis and scientific analysis etc. The phrase, “One image is equal to a thousand words”, is exactly fit as an image has more expressive capability than text messages. In all investigation, a photograph was always perceived as ‘proof of occurrence of an event’, due to a strong observation that, ‘seeing is believing ‘. Hence, images were considered as a ‘piece of truth’. Normally, an image is authentic if it is an originally recorded or captured from an actual scene or situation using any image capturing device. The captured image is expected to convey the original situation or scene at the source while capturing it in a real sense. In recent decades, due to the accessibility of modern photograph content altering software tools, it is very easy to transform the content of the image and therefore authenticity and integrity of the image is meager and tampering in digital images therefore does not require any specialist skills. This paper presents the modern methodological assessment and analysis of recent image forgery detection techniques. The various methods used in each stage of forgery detection techniques are also briefed. For an immediate reference, the tables of comparison are provided. The subject review paper is intended to help researchers to deliver useful understandings and modernized info about ongoing progress in forgery detection.

Siamese convolutional neural network‐based approach towards universal image forensics

This study proposes a novel deep learning-based method which can detect different types of image editing operations carried out on images. Unlike most of the existing methods, which can only detect the editing operations considered in the training stage, the proposed method can generalise to manipulations not seen in the training stage. The method is based on the classification of image pairs as either similarly or differently processed using a deep siamese neural network. Once the network learns features that can discriminate different editing operations, it can check whether an image is processed with an editing operation, not present in the training stage, using the one-shot classification strategy. An image forgery detection and localisation technique is also proposed using the trained siamese network. The experimental results show the efficacy of the proposed method in detecting different editing operations and also show the ability in detecting and localising image forgeries.

A copy-move image forgery detection technique based on tetrolet transform

Copy-move forgery is a common type of forgery in digital images. In copy-move forgery, one part of the image is replicated within the same image, generally at different location. For revival of trustworthiness of images, there is a need to develop an efficient and robust technique to detect such forgeries. This paper proposes a new copy-move image forgery detection technique based on Tetrolet transform. In this technique, initially the input image is divided into overlapping blocks, then four low-pass coefficients and twelve high-pass coefficients are extracted from each block by applying Tetrolet transform. Feature vectors are then sorted lexicographically, and similar blocks are identified by matching the extracted Tetrolet features. Experimental results show that the proposed technique can detect and locate the duplicated regions in the images very accurately, even when the copied regions have undergone some post-processing operations blurring, color reduction, adjustment of brightness and contrast, rotation, scaling, JPEG compression. In addition, it is also observed that the proposed technique is able to detect very small duplicated regions and multiple forgery cases, even when image is smooth.

An improved block based copy-move forgery detection technique

With the increase in demand for identification of authenticity of the digital images, researchers are widely studying the image forgery detection techniques. Copy-move forgery is amongst the commonly used forgery, which is performed by copying a part of an image and then pasting it on the same or different image. This results in the concealing of image content. Most of the existing copy-move forgery detection techniques are subjected to degradation in results, under the effect of geometric transformations. In this paper, a Discrete Cosine Transformation (DCT) and Singular Value Decomposition (SVD) based technique is proposed to detect the copy-move image forgery. DCT is used to transform the image from the spatial domain to the frequency domain and SVD is used to reduce the feature vector dimension. Combination of DCT and SVD makes the proposed scheme robust against compression, geometric transformations, and noise. For classification of images as forged or authentic, Support Vector Machine (SVM) classifier is used on the feature set. Once the image is detected as forged, then for the localization of forged region, K-means clustering is used on the feature vector. According to the distance threshold, similar blocks are identified and marked. The application of SVD provides stability and invariance from geometric transformations. Evaluation of the proposed scheme is done with and without post-processing operations on the images, both at the pixel level and image level. The proposed scheme outperforms the various state-of-the-art techniques of Copy-Move Forgery Detection (CMFD) in terms of accuracy, precision, recall and F1 parameters. Moreover, the proposed scheme also provides better results against rotation, scaling, noise and JPEG compression.

Image splicing detection using mask-RCNN

Digital images have become a dominant source of information and means of communication in our society. However, they can easily be altered using readily available image editing tools. In this paper, we propose a new blind image forgery detection technique which employs a new backbone architecture for deep learning which is called ResNet-conv. ResNet-conv is obtained by replacing the feature pyramid network in ResNet-FPN with a set of convolutional layers. This new backbone is used to generate the initial feature map which is then to train the Mask-RCNN to generate masks for spliced regions in forged images. The proposed network is specifically designed to learn discriminative artifacts from tampered regions. Two different ResNet architectures are considered, namely ResNet-50 and ResNet-101. The ImageNet, He_normal, and Xavier_normal initialization techniques are employed and compared based on convergence. To train a robust model for this architecture, several post-processing techniques are applied to the input images. The proposed network is trained and evaluated using a computer-generated image splicing dataset and found to be more efficient than other techniques.

A hybrid copy-move image forgery detection technique based on Fourier-Mellin and scale invariant feature transforms

In digital images, the most common forgery is copy-move image forgery in which some region(s) of an image is replicated within the image. The copy-move forgery detection (CMFD) techniques fall under two categories; keypoint-based and block-based. The keypoint-based techniques perform well under rotation and scaling but show very poor performance in the case of smooth images. On the contrary, the block-based techniques perform better in smooth images but are comparatively more time demanding. In this paper, a hybrid technique has been proposed by combining the block-based technique using Fourier-Mellin Transform (FMT) and a keypoint-based technique using Scale Invariant Feature Transform (SIFT). In this technique, the input image to be checked for forgery is first divided into texture and smooth regions. Then the keypoints are extracted from the texture part of the image using the SIFT descriptor, and the FMT is applied on the smooth part of the image. Extracted features are then matched to detect the duplicated regions of the image. The experimental results illustrate that the proposed technique performs better in comparison to other state-of-the-art CMFD techniques under various geometric transformations and post-processing operations in reasonable time.

Hierarchical Categorization and Review of Recent Techniques on Image Forgery Detection

Abstract Information in the form of the image conveys more details than any other form of information. Several software packages are available to manipulate the images so that the authenticity of the images is being questioned. Several image processing approaches are available to create fake images without leaving any visual clue about the forging operation. So, proper image forgery detection tools are required to detect such forgery images. Over the past few years, several research papers were published in the digital image forensics domain for detecting fake images, thus escalating the legitimacy of the images. This survey paper attempts to review the recent approaches proposed for detecting image forgery. Accordingly, several research papers related to image forgery detection are reviewed and analyzed. The taxonomy of image forgery detection techniques is presented, and the algorithms related to each technique are discussed. The comprehensive analysis is carried out based on the dataset used, software used for the implementation and the performance achievement. Besides, the research issues associated with every approach were scrutinized together with the recommendation for future work.

A copy-move image forgery detection technique based on Gaussian-Hermite moments

Images are one of the most prominently used digital information sharing medium now a days. Due to availability of state-of-the-art image editing tools it has become very easy to forge an image. Among various types of image forgeries, copy-move (region-duplication) forgery cases are emerging very frequently. In copy-move image forgery one or more regions of an image are replicated within the same image. In this paper, a new robust copy-move image forgery detection technique is proposed using Gaussian-Hermite Moments (GHM). The proposed technique divides the input image into overlapping blocks of fixed size and then the Gaussian-Hermite moments are extracted for each block. The matching of similar blocks is done by sorting all the features lexicographically. The experimental results show that the proposed technique can locate the copy-move forged regions in a forged image very accurately. The proposed technique shows promising results in the presence of various post-processing operations scaling, blurring, color reduction, adjustment of brightness, rotation, and JPEG compression.

A probabilistic framework for copy-move forgery detection based on Markov Random Field

Copy-move forgery is one of the most common kind of image tampering where some part of an image is copied, may be with minor modifications, pasted to another area of the same image. With the growing usage of images in todays life, image authenticity has become a vital issue and consequently many image forgery detection techniques have been presented. In this paper, for the first time, we propose to treat copy-move forgery detection as labeling problem in a Markov Random Field. To gain a proper balance between precision and speed, an over segmentation is performed as a preprocessing step to obtain superpixels which are then regarded as nodes of the markov network. Intelligent selection of unary and binary potentials let the maximum a posteriori labeling to be a precise map of the forged regions. Qualitative and quantitative comparison with the state-of-the-art methods using public benchmarks demonstrate that the proposed method can improve precision while keeping the processing demands low.

An Analysis of Image Forgery Detection Techniques

Society is becoming increasingly dependent on the internet and so does it become more and more vulnerable to harmful threats. These threats are becoming vigorous and continuously evolving. These threats distorted the authenticity of data transmitted through the internet. As we all completely or partially rely upon this transmitted data. Hence, its authenticity needs to be preserved. Images have the potential of conveying much more information as compared to the textual content. We pretty much believe everything that we see. In order to preserve/check the authenticity of images, image forgery detection techniques are expanding its domain. Detection of forgeries in digital images is in great need in order to recover the people’s trust in visual media. This paper is going to discuss different types of image forgery and blind methods for image forgery detection. It provides the comparative tables of various types of techniques to detect image forgery. It also gives an overview of different datasets used in various approaches of forgery detection.