Digital Image Authentication Research Articles

PIM-Net: Progressive Inconsistency Mining Network for image manipulation localization

The content authenticity and reliability of digital images have promoted the research on image manipulation localization (IML). Most current deep learning-based methods focus on extracting global or local tampering features for identifying forged regions. These features usually contain semantic information and lead to inaccurate detection results for non-object or incomplete semantic tampered regions. In this study, we propose a novel Progressive Inconsistency Mining Network (PIM-Net) for effective IML. Specifically, PIM-Net consists of two core modules, the Inconsistency Mining Module (ICMM) and the Progressive Fusion Refinement module (PFR). ICMM models the inconsistency between authentic and forged regions at two levels, i.e., pixel correlation inconsistency and region attribute incongruity, while avoiding the interference of semantic information. Then PFR progressively aggregates and refines extracted inconsistent features, which in turn yields finer and pure localization responses. Extensive qualitative and quantitative experiments on five benchmarks demonstrate PIM-Net’s superiority to current state-of-the-art IML methods. Code: https://github.com/ningnbai/PIM-Net.

An efficient digital image forgery detection using Pelican search optimisation-based DCNN

ABSTRACT In the digital age, images are widely used to document events, provide evidence, and communicate information. Ensuring the authenticity of digital images is crucial to maintaining trust and integrity in various domains, including journalism, forensics, legal proceedings, and historical documentation. In this research, a PeCA-DCNN model for digital image detection is proposed, employing a hybrid approach. Initially, the data from image forgery databases is collected and preprocessed to eliminate noise and artefacts. Subsequently, the Viola–Jones algorithm detects frontal faces, and feature extraction is performed using pre-trained models VGG-16 and Resnet-101. To reduce the computational overhead, feature extraction is performed and generates a feature vector. The PeCA algorithm, combined with an adaptive self-boosted DCNN, is used to classify fake and genuine images. The PeCA algorithm enhances model performance by adjusting classifier parameters’ weights and biases. When evaluating the PeCA-DCNN, significant improvements in accuracy, sensitivity, and specificity are obtained with enhancement rates of 1.48, 3.06, and 0.05 in an 80% training scenario, and 3.92, 3.24, and 2.22 in k-fold cross-validation. These results demonstrate the effectiveness of the proposed approach compared to existing techniques.

Detecting Artificial Intelligence-Generated images via deep trace representations and interactive feature fusion

The detection of Artificial Intelligence-Generated (AIG) images plays an important role in verifying the authenticity and originality of digital images. However, recent advancements in state-of-the-art image generation methods have significantly challenged the ability to differentiate AIG images from natural photographs (NP). To address this issue, a novel approach based on deep trace representations and dual-branch interactive feature fusion is presented. Firstly, a global feature extraction module that leverages attention-based MobileViT (AT-MobileViT) is designed to learn the deep representations of the global trace information. Besides, we apply multiple enhanced residual blocks to extract discriminative multi-scale features. After that, a low-level feature extraction module incorporating a channel-spatial attention (CSA) block is also carefully employed to enhance the learning of trace representations. To facilitate the capture of complementary information between features, a dual-branch interactive feature fusion module is introduced by reshaping feature vectors into interactive matrices. By conducting experiments on both seen and unseen images, results demonstrate the better performance and robustness of the proposed method.

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.

Mengeksplorasi Praktik Personal Digital Archiving (PDA) dalam Fenomena New Analog Photography di Instagram: Membangun Personal Branding, Pencarian Autentisitas dan Detoksifikasi Digital

This article discusses the phenomenon of the rise of analog photography called new analogphotography on Instagram. Apart from discussing the phenomenon of new analog photography, this article also discusses the practice of personal digital archiving (PDA), which is applied to this phenomenon. Social media is a platform for storing personal digital archives, which has been done by all humans in the internet era and electronic devices have become an integral part of their lives. The method used in writing articles uses a qualitative case study approach. The aim of this article is to find the causes of the new analog photography phenomenon, namely the millennial generation and Gen Z who are very active on social media and have personal branding awareness. The exploration carried out by the millennial generation and Gen Z who have an interest in photography to search for the authenticity of digital presence images and rituals using analog cameras can be categorized as digital detoxification. The limitation of this article is that the number of respondents obtained in quantity is not yet representative. It is hoped that future research will be able to gather more respondents with more varied demographics to be able to map the distribution of this phenomenon. This article explains that photography, which is a form of visual archive, is related to the study of social, cultural and technological topics. So that it provides a new perspective for developing the field of archives, especially special-form archives.

Digital Image Authenticity Assessment Using Deep Learning

Its super important to find fake pictures online to make sure things are true so we came up with a smart idea using something called a convolutional neural network, CNN is like a really smart detective for pictures. It checks out lots of pictures, some real and some fake and learns how to tell them apart by looking at all the tiny details Before the detective work, we make all the pictures the same size, like putting them in the same-sized frame We also use a special tool called error level analysis (Ela). Ela helps us by showing parts of the pictures that might have been changed its like searching for clues in a detective movie We looked at a bunch of pictures, over 12,000 of them We made sure to have a mix of real ones like trees and people 7492 of them and some that were changed to trick people, 5123 of them this way, our detective could learn from lots of different situations our system is really good at finding fake pictures because it uses fancy math and clever techniques We tested it a lot to make sure it works in different situations, like when someone copies part of a picture or changes how it looks and guess what? Our tests showed that our system is great at finding fakes In the end, our idea helps make sure pictures online are real, which is super important in today’s world of digital pictures.

Color Patterns And Enhanced Texture Learning For Detecting Computer-Generated Images

Abstract Detection of computer-generated (CG) images can reveal the authenticity and originality of digital images. However, recent cutting-edge image generation methods make it very difficult to distinguish CG images from natural photographs. In this paper, a novel method based on color patterns and enhanced texture learning is proposed to tackle this problem. We designed and implemented the backbone network with a separation-fusion learning strategy by constructing a multi-branch neural network. The luminance and chrominance patterns in dual-color spaces (RGB and YCbCr) are leveraged to achieve a robust representation of image differences. A channel-spatial attention module and a global texture enhancement module are also integrated into a backbone network to enhance the learning of inherent traces. Experiments on several commonly used benchmark datasets and a newly constructed dataset with more realistic and diverse images demonstrate that the proposed algorithm outperforms state-of-the-art competitors by a large margin.

CNN-based Approach for Robust Detection of Copy-Move Forgery in Images

With the rise of high-quality forged images on social media and other platforms, there is a need for algorithms that can recognize the originality. Detecting copy-move forgery is essential for ensuring the authenticity and integrity of digital images, preventing fraud and deception, and upholding the law. Copy-move forgery is the act of duplicating and pasting a portion of an image to another location within the same image. To address these issues, we propose two deep learning approaches - one using a custom architecture and the other using transfer learning. We test our method against a number of benchmark datasets and demonstrate that, in terms of accuracy and robustness against various types of image distortions, it outperforms current state-of-the-art methods. Our proposed method has applications in digital forensics, copyright defence, and image authenticity.

A block-based fragile watermarking scheme for digital image authentication and tamper recovery

A block-based fragile watermarking scheme for digital image authentication and tamper recovery

Improving Detection of DeepFakes through Facial Region Analysis in Images

In the evolving landscape of digital media, the discipline of media forensics, which encompasses the critical examination and authentication of digital images, videos, and audio recordings, has emerged as an area of paramount importance. This heightened significance is predominantly attributed to the burgeoning concerns surrounding the proliferation of DeepFakes, which are highly realistic and manipulated media content, often created using advanced artificial intelligence techniques. Such developments necessitate a profound understanding and advancement in media forensics to ensure the integrity of digital media in various domains. Current research endeavours are primarily directed towards addressing a common challenge observed in DeepFake datasets, which pertains to the issue of overfitting. Many suggested remedies centre around the application of data augmentation methods, with a frequently adopted strategy being the incorporation of random erasure or cutout. This method entails the random removal of sections from an image to introduce diversity and mitigate overfitting. Generating disparities between the altered and unaltered images serves to inhibit the model from excessively adapting itself to individual samples, thus leading to more favourable results. Nonetheless, the stochastic nature of this approach may inadvertently obscure facial regions that harbour vital information necessary for DeepFake detection. Due to the lack of guidelines on specific regions for cutout, most studies use a randomised approach. However, in recent research, face landmarks have been integrated to designate specific facial areas for removal, even though the selection remains somewhat random. Therefore, there is a need to acquire a more comprehensive insight into facial features and identify which regions hold more crucial data for the identification of DeepFakes. In this study, the investigation delves into the data conveyed by various facial components through the excision of distinct facial regions during the training of the model. The goal is to offer valuable insights to enhance forthcoming face removal techniques within DeepFake datasets, fostering a deeper comprehension among researchers and advancing the realm of DeepFake detection. Our study presents a novel method that uses face cutout techniques to improve understanding of key facial features crucial in DeepFake detection. Moreover, the method combats overfitting in DeepFake datasets by generating diverse images with these techniques, thereby enhancing model robustness. The developed methodology is validated against publicly available datasets like FF++ and Celeb-DFv2. Both face cutout groups surpassed the Baseline, indicating cutouts improve DeepFake detection. Face Cutout Group 2 excelled, with 91% accuracy on Celeb-DF and 86% on the compound dataset, suggesting external facial features’ significance in detection. The study found that eyes are most impactful and the nose is least in model performance. Future research could explore the augmentation policy’s effect on video-based DeepFake detection.

Exposing fake images generated by text-to-image diffusion models

Text-to-image diffusion models (DM) have posed unprecedented challenges to the authenticity and integrity of digital images, which makes the detection of computer-generated images one of the most important image forensics techniques. However, the detection of images generated by text-to-image diffusion models is rarely reported in the literature. To tackle this issue, we first analyze the acquisition process of DM images. Then, we construct a hybrid neural network based on attention-guided feature extraction (AGFE) and vision transformers (ViTs)-based feature extraction (ViTFE) modules. An attention mechanism is adopted in the AGFE module to capture long-range feature interactions and boost the representation capability. ViTFE module containing sequential MobileNetv2 block (MNV2) and MobileViT blocks are designed to learn global representations. By conducting extensive experiments on different types of generated images, the results demonstrate the effectiveness and robustness of our method in exposing fake images generated by text-to-image diffusion models.

Medical Image Encryption: A Comprehensive Review

In medical information systems, image data can be considered crucial information. As imaging technology and methods for analyzing medical images advance, there will be a greater wealth of data available for study. Hence, protecting those images is essential. Image encryption methods are crucial in multimedia applications for ensuring the security and authenticity of digital images. Recently, the encryption of medical images has garnered significant attention from academics due to concerns about the safety of medical communication. Advanced approaches, such as e-health, smart health, and telemedicine applications, are employed in the medical profession. This has highlighted the issue that medical images are often produced and shared online, necessitating protection against unauthorized use.

A survey on deep learning-based image forgery detection

Image is known as one of the communication tools between humans. With the development and availability of digital devices such as cameras and cell phones, taking images has become easy anywhere. Images are used in many medical, forensic medicine, and judiciary applications. Sometimes images are used as evidence, so the authenticity and reliability of digital images are increasingly important. Some people manipulate images by adding or deleting parts of an image, which makes the image invalid. Therefore, image forgery detection and localization are important. The development of image editing tools has made this issue an important problem in the field of computer vision. In recent years, many different algorithms have been proposed to detect forgery in the image and pixel levels. All these algorithms are categorized into two main methods: traditional and deep-learning methods. The deep learning method is one of the important branches of artificial intelligence science. This method has become one of the most popular methods in most computer vision problems due to the automatic identification and prediction process and robustness against geometric transformations and post-processing operations. In this study, a comprehensive review of image forgery types, benchmark datasets, evaluation metrics in forgery detection, traditional forgery detection methods, discovering the weaknesses and limitations of traditional methods, forgery detection with deep learning methods, and the performance of this method is presented. According to the expansion of deep-learning methods and their successful performance in most computer vision problems, our main focus in this study is forgery detection based on deep-learning methods. This survey can be helpful for a researcher to obtain a deep background in the forgery detection field.

A novel reversible fragile watermarking method in DWT domain for tamper localization and digital image authentication

Medical images are extremely valuable and sensitive data because of their significance in research, diagnosis, and treatment. However, the transmission of medical images over an unsecured network can lead to security issues such as modification, removal, copying, and unauthorized access. For this reason, the protection of medical information is of great importance. This paper proposes a novel reversible fragile digital watermarking method for tamper localization and authenticity and integrity protection in medical images. In the proposed method, the host image is firstly divided into 8×8 non-overlapped blocks. Then the image is transformed to the frequency domain by applying a Discrete Wavelet Transform, and the watermark information is embedded. Finally, the image is converted from the frequency domain to the spatial domain using an inverse Discrete Wavelet Transform. During this transformation, an intelligent optimization technique is used to correct the rounding errors and obtain the watermarked image with the highest level of imperceptibility. The experimental results demonstrate that the proposed method offers a watermarked image with high imperceptibility, extracts the host image lossless during the watermark extraction phase, and performs tamper localization with high precision.

Altered Region Identification in Image Files using Copy-Move Forgery Detection Technique

Nowadays, digital images and videos have high importance because they have become the primary carriers of information. However, the easy availability of powerful image editing software has made it possible to manipulate and edit digital images and videos, leading to a loss of trust in their authenticity. It is possible to add or remove important features from an image without leaving any obvious traces of tampering. To detect this such type of forgeries, the proposed method involves dividing the image into overlapping blocks of the same size, extracting a feature for each block, and representing it as a vector. The vectors are then sorted using radix sort, and the difference in the positions of adjacent feature vectors in the sorted list is computed to obtain a shift vector. This method can even identify tampered parts of an image that have been enhanced or retouched to merge with the background or saved in a lossy format like JPEG. Several forged images were tested to demonstrate the effectiveness of this proposed method. Its application has significant implications in fields like forensics, journalism, and medical imaging to ensure the authenticity of digital images and videos.

Robust Watermarking Techniques for the Authentication and Copyright Protection of Digital Images: A Survey

Digital image authentication and security are crucial concerns for the digital revolution since any image may be readily tampered with. Digital watermarking schemes were since been utilized to tackle a range of problems over time and involving the verification of digital images and copyright protection. In a real-world setting, watermarked contents are frequently subject to a succession of assaults which makes it necessary to strike a balance between resilience and imperceptibility in the face of attacks. There are numerous hazards to this, and numerous watermarking techniques have been created to counter them. Data with less perceptual distortion should be able to be included in a real watermarking technique in addition to restoring the original cover content. As a result, robustness, imperceptibility, and security are three crucial criteria for digital content'sauthentication and copyrightpreservation. This study therefore presents a careful review of some cutting-edge watermarking technologies used for copyright protection and authentication so as to identify their strengths and limitations. For clarity, this study shall explain some basic concepts of digital watermarking as well as the various forms of attacks on watermarks. The contributions from this work will be useful to researchers aim at developing efficient watermarking techniques

Image Forgery Detection Using Neural Network In Python

The advancements of technology in every aspect of the current age are leading to the misuse of data. Researchers, therefore, face the challenging task of identifying these manipulated forms of data and distinguishing the real data from the manipulated. Splicing is one of the most common techniques used for digital image tampering; a selected area copied from the same or another image is pasted in an image. Image forgery detection is considered a reliable way to verify the authenticity of digital images. In this study, we proposed an approach based on the state-of-the-art deep learning architecture of ResNet50v2. The proposed model takes image batches as input and utilizes the weights of a YOLO convolutional neural network (CNN) by using the architecture of ResNet50v2. In this study, we used the CASIA_v1 and CASIA_v2 benchmark datasets, which contain two distinct categories, original and forgery, to detect image splicing. We used 80% of the data for the training and the remaining 20% for testing purposes. We also performed a comparative analysis between existing approaches and our proposed system. We evaluated the performance of our technique with the CASIA_v1 and CASIA_v2 datasets. Since the CASIA_v2 dataset is more comprehensive compared to the CASIA_v1 dataset, we obtained 99.3% accuracy for the fine-tuned model using transfer learning and 81% accuracy without transfer learning with the CASIA_v2 dataset. The results show the superiority of the proposed system

A Block-Based Fragile Watermarking Scheme for Digital Image Authentication and Tamper Recovery

A Block-Based Fragile Watermarking Scheme for Digital Image Authentication and Tamper Recovery

An Overview of Copy Move Forgery Detection Approaches

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.

A robust detection and localization technique for copy-move forgery in digital images

Image forensic analysis becomes a major role in the field of digital image security due to tampering and forgery. The image forgery violates the authenticity and ownership of digital images. Copy-move forgery considers a significant kind of image forensic analysis algorithm. In this kind, the forger copies a part of an original image and then pastes it into the selected position from the same image. The purpose of forgery is to hide or highlight a specific region of the original image. To detect copy-move forgery, there are two traditional techniques: block-based and keypoint-based. The main drawback of the keypoint-based technique is the insufficient features for the small and flat regions, which causes undetected forgery. In contrast, the block-based technique has intensive processing. Therefore, this paper proposes a robust scheme that overcomes the drawbacks of the above techniques and maintains their advantages. This scheme adopts three connected stages, the first detects the initial duplicated regions using the SURF-HOG detector and descriptor. Subsequently, the second stage localizes the primary matched regions by SLIC segmentation and then selects the suspicious neighbor regions to be combined with primary regions to obtain the active regions. In the third stage, the block-based technique adopts overlapping Zernike moments to extract sufficient key points from the produced active regions. In the final stage, the duplicated regions are classified into authentic or forged regions. The proposed scheme provides not only forgery detection but also localization and recognition for the duplicated regions. The experimental results show that the proposed scheme is fast and has high accuracy for forgery detection and localization, at least 93.75, and 7.25 in terms of True Positive and False Positive Rates. Moreover, the scheme has high robustness under various conditions and attacks such as geometric transformation attacks and compound photometric attacks. The proposed scheme can be used in sensitive applications such as cybercrime detection and adopted as evidence in the courts.