Recent advances in digital image manipulation detection techniques: A brief review

Bibliometric evaluation of Forensic Science International as a scholarly journal within the subject category legal medicine

This article presents a bibliometric evaluation of Forensic Science International (FSI) as a scholarly journal within the "legal medicine" subject category. Citation data were retrieved from Science Citation Index (SCI) and Journal Citation Reports (JCR), both of which are part of the Web-of-Science (WOS) database. The most cited articles in FSI were identified along with the most prolific authors. The current journal impact factor (JIF) of FSI is 2.2, which was in good agreement with the 5-year JIF of 2.3. FSI was ranked fourth among 17 journals within the legal medicine subject category. Since 1979, a total of 209 FSI articles were cited over 100 times and the H-index for times cited was 125. Although widely used in academia, bibliometric methods might also prove useful in jurisprudence, such as when evaluating the research and publications of people proposed as expert witnesses.

S. An image and text-based multimodal model for detecting fake news in OSN's

Digital Mass Media has become the new paradigm of communication that revolves around online social networks. The increase in the utilization of online social networks (OSNs) as the primary source of information and the increase of online social platforms providing such news has increased the scope of spreading fake news. People spread fake news in multimedia formats like images, audio, and video. Visual-based news is prone to have a psychological impact on the users and is often misleading. Therefore, Multimodal frameworks for detecting fake posts have gained demand in recent times. This paper proposes a framework that flags fake posts with Visual data embedded with text. The proposed framework works on data derived from the Fakeddit dataset, with over 1 million samples containing text, image, metadata, and comments data gathered from a wide range of sources, and tries to exploit the unique features of fake and legitimate images. The proposed framework has different architectures to learn visual and linguistic models from the post individually. Image polarity datasets, derived from Flickr, are also considered for analysis, and the features extracted from these visual and text-based data helped in flagging news. The proposed fusion model has achieved an overall accuracy of 91.94%, Precision of 93.43%, Recall of 93.07%, and F1-score of 93%. The experimental results show that the proposed Multimodality model with Image and Text achieves better results than other state-of-art models working on a similar dataset.

A New Method for Image Protection Using Periodic Haar Piecewise-Linear Transform and Watermarking Technique

The paper presents a novel data-embedding method based on the Periodic Haar Piecewise-Linear (PHL) transform. The theoretical background behind the PHL transform concept is introduced. The proposed watermarking method assumes embedding hidden information in the PHL transform domain using the luminance channel of the original image. The watermark is embedded by modifying the coefficients with relatively low values. The proposed method was verified based on the measurement of the visual quality of an image with a watermark with respect to the length of the embedded information. In addition, the bit error rate (BER) is also considered for different sizes of a watermark. Furthermore, a method for the detection of image manipulation is presented. The elaborated technique seems to be suitable for applications in digital signal and image processing where high imperceptibility and low BER are required, and information security is of high importance. In particular, this method can be applied in systems where the sensitive data is transmitted or stored and needs to be protected appropriately (e.g., in medical image processing).

Recent Advances in Counterfeit Art, Document, Photo, Hologram, and Currency Detection Using Hyperspectral Imaging

Forgery and tampering continue to provide unnecessary economic burdens. Although new anti-forgery and counterfeiting technologies arise, they inadvertently lead to the sophistication of forgery techniques over time, to a point where detection is no longer viable without technological aid. Among the various optical techniques, one of the recently used techniques to detect counterfeit products is HSI, which captures a range of electromagnetic data. To aid in the further exploration and eventual application of the technique, this study categorizes and summarizes existing related studies on hyperspectral imaging and creates a mini meta-analysis of this stream of literature. The literature review has been classified based on the product HSI has used in counterfeit documents, photos, holograms, artwork, and currency detection.

Image copy-move forgery detection and localization based on super-BPD segmentation and DCNN

With the increasing importance of image information, image forgery seriously threatens the security of image content. Copy-move forgery detection (CMFD) is a greater challenge because its abnormality is smaller than other forgeries. To solve the problem that the detection results of the most image CMFD based on convolutional neural networks (CNN) have relatively low accuracy, an image copy-move forgery detection and localization based on super boundary-to-pixel direction (super-BPD) segmentation and deep CNN (DCNN) is proposed: SD-Net. Firstly, the segmentation technology is used to enhance the connection between the same or similar image blocks, improving the detection accuracy. Secondly, DCNN is used to extract image features, replacing conventional hand-crafted features with automatic learning features. The feature pyramid is used to improve the robustness to the scaling attack. Thirdly, the image BPD information is used to optimize the edges of rough detected image and obtain final detected image. The experiments proved that the SD-Net could detect and locate multiple, rotated, and scaling forgery well, especially large-level scaling forgery. Compared with other methods, the SD-Net is more accurately located and robust to various post-processing operations: brightness change, contrast adjustments, color reduction, image blurring, JPEG compression, and noise adding.

Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection

Due to the exponential growth of high-quality fake photos on social media and the Internet, it is critical to develop robust forgery detection tools. Traditional picture- and video-editing techniques include copying areas of the image, referred to as the copy-move approach. The standard image processing methods physically search for patterns relevant to the duplicated material, restricting the usage in enormous data categorization. On the contrary, while deep learning (DL) models have exhibited improved performance, they have significant generalization concerns because of their high reliance on training datasets and the requirement for good hyperparameter selection. With this in mind, this article provides an automated deep learning-based fusion model for detecting and localizing copy-move forgeries (DLFM-CMDFC). The proposed DLFM-CMDFC technique combines models of generative adversarial networks (GANs) and densely connected networks (DenseNets). The two outputs are combined in the DLFM-CMDFC technique to create a layer for encoding the input vectors with the initial layer of an extreme learning machine (ELM) classifier. Additionally, the ELM model's weight and bias values are optimally adjusted using the artificial fish swarm algorithm (AFSA). The networks' outputs are supplied into the merger unit as input. Finally, a faked image is used to identify the difference between the input and target areas. Two benchmark datasets are used to validate the proposed model's performance. The experimental results established the proposed model's superiority over recently developed approaches.

Image manipulation in scholarly publications: are there ways to an automated solution?

Purpose

This paper provides an introduction to research in the field of image forensics and asks whether advances in the field of algorithm development and digital forensics will facilitate the examination of images in the scientific publication process in the near future.

Design/methodology/approach

This study looks at the status quo of image analysis in the peer review process and evaluates selected articles from the field of Digital Image and Signal Processing that have addressed the discovery of copy-move, cut-paste and erase-fill manipulations.

Findings

The article focuses on forensic research and shows that, despite numerous efforts, there is still no applicable tool for the automated detection of image manipulation. Nonetheless, the status quo for examining images in scientific publications remains visual inspection and will likely remain so for the foreseeable future. This study summarizes aspects that make automated detection of image manipulation difficult from a forensic research perspective.

Research limitations/implications

Results of this study underscore the need for a conceptual reconsideration of the problems involving image manipulation with a view toward the need for interdisciplinary collaboration in conjunction with library and information science (LIS) expertise on information integrity.

Practical implications

This study not only identifies a number of conceptual challenges but also suggests areas of action that the scientific community can address in the future.

Originality/value

Image manipulation is often discussed in isolation as a technical challenge. This study takes a more holistic view of the topic and demonstrates the necessity for a multidisciplinary approach.

A deep-learning-based image forgery detection framework for controlling the spread of misinformation

Purpose

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

Design/methodology/approach

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

Findings

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

Research limitations/implications

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

Practical implications

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

Social implications

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

Originality/value

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

Plagiarism detection and prevention: a primer for researchers

Plagiarism is an ethical misconduct affecting the quality, readability, and trustworthiness of scholarly publications. Improving researcher awareness of plagiarism of words, ideas, and graphics is essential for avoiding unacceptable writing practices. Global editorial associations have publicized their statements on strategies to clean literature from redundant, stolen, and misleading information. Consulting related documents is advisable for upgrading author instructions and warning plagiarists of academic and other consequences of the unethical conduct. A lack of creative thinking and poor academic English skills are believed to compound most instances of redundant and "copy-and-paste" writing. Plagiarism detection software largely relies on reporting text similarities. However, manual checks are required to reveal inappropriate referencing, copyright violations, and substandard English writing. Medical researchers and authors may improve their writing skills and avoid the same errors by consulting the list of retractions due to plagiarism which are tracked on the PubMed platform and discussed on the Retraction Watch blog.

Copy-Move Forgery Detection (CMFD) Using Deep Learning for Image and Video Forensics

With the exponential growth of high-quality fake images in social networks and media, it is necessary to develop recognition algorithms for this type of content. One of the most common types of image and video editing consists of duplicating areas of the image, known as the copy-move technique. Traditional image processing approaches manually look for patterns related to the duplicated content, limiting their use in mass data classification. In contrast, approaches based on deep learning have shown better performance and promising results, but they present generalization problems with a high dependence on training data and the need for appropriate selection of hyperparameters. To overcome this, we propose two approaches that use deep learning, a model by a custom architecture and a model by transfer learning. In each case, the impact of the depth of the network is analyzed in terms of precision (P), recall (R) and F1 score. Additionally, the problem of generalization is addressed with images from eight different open access datasets. Finally, the models are compared in terms of evaluation metrics, and training and inference times. The model by transfer learning of VGG-16 achieves metrics about 10% higher than the model by a custom architecture, however, it requires approximately twice as much inference time as the latter.

Video Forensics: Identifying Colorized Images Using Deep Learning

In recent years there has been a significant increase in images and videos circulating in social networks and media, edited with different techniques, including colorization. This has a negative impact on the forensic field because it is increasingly difficult to discern what is original content and what is fake. To address this problem, we propose two models (a custom architecture and a transfer-learning-based model) based on CNNs that allows a fast recognition of the colorized images (or videos). In the experimental test, the effect of three hyperparameters on the performance of the classifier were analyzed in terms of HTER (Half Total Error Rate). The best result was found for the Adam optimizer, with a dropout of 0.25 and an input image size of 400 × 400 pixels. Additionally, the proposed models are compared with each other in terms of performance and inference times and with some state-of-the-art approaches. In terms of inference times per image, the proposed custom model is 12x faster than the transfer-learning-based model; however, in terms of precision (P), recall and F1-score, the transfer-learning-based model is better than the custom model. Both models generalize better than other models reported in the literature.

News authentication and tampered images: evaluating the photo-truth impact through image verification algorithms

Photos have been used as evident material in news reporting almost since the beginning of Journalism. In this context, manipulated or tampered pictures are very common as part of informing articles, in today's misinformation crisis. The current paper investigates the ability of people to distinguish real from fake images. The presented data derive from two studies. Firstly, an online cross-sectional survey (N = 120) was conducted to analyze ordinary human skills in recognizing forgery attacks. The target was to evaluate individuals' perception in identifying manipulated visual content, therefore, to investigate the feasibility of “crowdsourced validation”. This last term refers to the process of gathering fact-checking feedback from multiple users, thus collaborating towards assembling pieces of evidence on an event. Secondly, given that contemporary veracity solutions are coupled with both journalistic principles and technology developments, an experiment in two phases was employed: a) A repeated measures experiment was conducted to quantify the associated abilities of Media and Image Experts (N = 5 + 5) in detecting tampering artifacts. In this latter case, image verification algorithms were put into the core of the analysis procedure to examine their impact on the authenticity assessment task. b) Apart from conducting interview sessions with the selected experts and their proper guidance in using the tools, a second experiment was also deployed on a larger scale through an online survey (N = 301), aiming at validating some of the initial findings. The primary intent of the deployed analysis and their combined interpretation was to evaluate image forensic services, offered as real-world tools, regarding their comprehension and utilization by ordinary people, involved in the everyday battle against misinformation. The outcomes confirmed the suspicion that only a few subjects had prior knowledge of the implicated algorithmic solutions. Although these assistive tools often lead to controversial or even contradictory conclusions, their experimental treatment with the systematic training in their proper use boosted the participants' performance. Overall, the research findings indicate that the scores of successful detections, relying exclusively on human observations, cannot be disregarded. Hence, the ultimate challenge for the “verification industry” should be to balance between forensic automations and the human experience, aiming at defending the audience from inaccurate information propagation.

Recent Advances in Digital Multimedia Tampering Detection for Forensics Analysis

In the digital multimedia era, digital forensics is becoming an emerging area of research thanks to the large amount of image and video files generated. Ensuring the integrity of such media is of great importance in many situations. This task has become more complex, especially with the progress of symmetrical and asymmetrical network structures which make their authenticity difficult. Consequently, it is absolutely imperative to discover all possible modes of manipulation through the development of new forensics detector tools. Although many solutions have been developed, tamper-detection performance is far from reliable and it leaves this problem widely open for further investigation. In particular, many types of multimedia fraud are difficult to detect because some evidences are not exploited. For example, the symmetry and asymmetry inconsistencies related to visual feature properties are potential when applied at multiple scales and locations. We explore here this topic and propose an understandable soft taxonomy and a deep overview of the latest research concerning multimedia forgery detection. Then, an in-depth discussion and future directions for further investigation are provided. This work offers an opportunity for researchers to understand the current active field and to help them develop and evaluate their own image/video forensics approaches.