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Enhancing Sensing and Imaging Capabilities Through Surface Plasmon Resonance for Deepfake Image Detection
Plasmonic nanomaterials have revolutionized sensing and imaging technologies due to their unique optical properties, particularly surface plasmon resonance (SPR). These materials offer enhanced sensitivity and resolution, making them promising candidates for applications in deepfake image detection, where accurate authentication of digital content is crucial. This work presents the application of plasmonic nanomaterials in enhancing sensing and imaging capabilities for deepfake detection. Gold nanoparticles functionalized with specific ligands are employed to exploit SPR effects, enabling sensitive detection of minute alterations in image content. A spectroscopic setup is utilized to measure the SPR shifts corresponding to changes induced by deepfake manipulations. Experimental results demonstrate that the SPR-based sensing approach achieves a detection accuracy of over 95% in distinguishing deepfake images from authentic ones. The SPR sensor exhibits a high signal-to-noise ratio, providing robust performance even in complex imaging scenarios with varying lighting conditions and image resolutions. Plasmonic nanomaterials, leveraging SPR, offer a reliable method for enhancing deepfake image detection capabilities. The demonstrated high accuracy and sensitivity underscore their potential in combating digital media forgery, contributing to the development of more secure and trustworthy authentication systems for visual content.
Journal Article
Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers
Passive image forgery detection methods that identify forgeries without prior knowledge have become a key research focus. In copy-move forgery, the assailant intends to hide a portion of an image by pasting other portions of the same image. The detection of such manipulations in images has great demand in legal evidence, forensic investigation, and many other fields. The paper aims to present copy-move forgery detection algorithms with the help of advanced feature descriptors, such as local ternary pattern, local phase quantization, local Gabor binary pattern histogram sequence, Weber local descriptor, and local monotonic pattern, and classifiers such as optimized support vector machine and optimized NBC. The proposed algorithms can classify an image efficiently as either copy-move forged or authenticated, even if the test image is subjected to attacks such as JPEG compression, scaling, rotation, and brightness variation. CoMoFoD, CASIA, and MICC datasets and a combination of CoMoFoD and CASIA datasets images are used to quantify the performance of the proposed algorithms. The proposed algorithms are more efficient than state-of-the-art algorithms even though the suspected image is post-processed.
Journal Article
Digital image and video watermarking: methodologies, attacks, applications, and future directions
In recent years, internet technology has grown in advance, and multimedia data-sharing growth rates have skyrocketed. As a result, protecting multimedia data in digital networks has become a significant problem. Multimedia data such as audio, text, video, and image are highly used as a data-sharing communication system which demands security, particularly in image and video. Digital watermarking is the one solution that has gained widespread recognition over the past two decades for data embedding in image and video, a key tactic in multimedia tamper detection and recovery. The review tells about the growth rate and data breaches on multimedia data across different applications, which raises the issue of multimedia security. Notably, social network platforms are highly targeted due to their rapid growth, which has created opportunities for data breaches and multimedia manipulation. Here, the forensic field comes into play, where some data-hiding strategies are used to look for evidence of tampering. Even though watermarking techniques can attain security in tamper detection, they face some issues and challenges across various applications. This motivated us to analyze the existing work carried out by data hiding watermarking techniques in the field of multimedia tamper detection in detail and the gap analyzed. Overall, dataset availability, watermarking performance quality metrics, and several image-processing attacks are all explicitly mentioned. This review paper discusses a comprehensive study of the existing system in the field of tamper detection (both in Image and Video) in detail. Also, the development of existing watermarking techniques, issues, and challenges are covered in detail in this paper.
Journal Article
A novel deep learning framework for copy-moveforgery detection in images
In this era of technology, digital images turn out to be ubiquitous in a contemporary society and they can be generated and manipulated by a wide variety of hardware and software technologies. Copy-move forgery is considered as an image tampering technique that aims to generate manipulated tampered images by concealing unwanted objects or reproducing desirable objects within the same image. Therefore, image content authentication has become an essential demand. In this paper, an innovative design for automatic detection of copy-move forgery based on deep learning approaches is proposed. A Convolutional Neural Network (CNN) is specifically designed for Copy-Move Forgery Detection (CMFD). The CNN is exploited to learn hierarchical feature representations from input images, which are used for detecting the tampered and original images. The extensive experiments demonstrate that the proposed deep CMFD algorithm outperforms the traditional CMFD systems by a considerable margin on the three publicly accessible datasets: MICC-F220, MICC-F2000, and MICC-F600. Furthermore, the three datasets are incorporated and joined to the SATs-130 dataset to form new combinations of datasets. An accuracy of 100% has been achieved for the four datasets. This proves the robustness of the proposed algorithm against a diversity of known attacks. For better evaluation, comparative results are included.
Journal Article
DCT based efficient fragile watermarking scheme for image authentication and restoration
Due to rapid development of Internet and computer technology, image authentication and restoration are very essential, especially when it is utilized in forensic science, medical imaging and evidence of court. A quantization and Discrete Cosine Transform(DCT) based self-embedding fragile watermarking scheme with effective image authentication and restoration quality is proposed in this paper. In this scheme, the cover image is divided in size of 2×2 non-overlapping blocks. For each block twelve bits watermark are generated from the five most significant bits (MSBs) of each pixel and are embedded into the three least significant bits (LSBs) of the pixels corresponding to the mapped block. The proposed scheme uses two levels encoding for content restoration bits generation. The restoration is achievable with high PSNR and NCC up to 50 % tampering rate. The experimental results demonstrate that the proposed scheme not only outperforms high quality restoration effectively, but also removes the blocking artifacts and improves the accuracy of tamper localization due to use of very small size blocks.
Journal Article
Shallow-FakeFaceNet: A CNN-Based Detection Framework for GAN-Generated and Handcrafted Facial Forgeries
With the rapid advancement of digital media technologies, facial image manipulation has become increasingly sophisticated. Both handcrafted editing tools and deep generative models such as Generative Adversarial Networks (GANs) can produce convincingly fake facial images, posing significant threats like misinformation and identity fraud. In this study, we introduce a novel Handcrafted Facial Manipulation (HFM) dataset, containing 1,527 manually edited images across multiple modification types and complexity levels. To detect these fakes along with GAN-generated images, we propose a lightweight neural network called Shallow-FakeFaceNet (SFFN), optimized for low-resolution images (64×64 and 128×128). The detection pipeline includes MTCNN-based face cropping, noise filtering, GAN-based facial super-resolution for enhancing small images, and extensive image augmentation using both Keras and ImgAug. Unlike prior works that rely on fragile metadata, our model operates solely on RGB image data, making it robust against common forgery tactics. Experimental results show that SFFN achieves an AUROC of 72.52% on handcrafted fakes and 93.99% on GAN-generated faces, outperforming several state-of-the-art models. This approach offers a practical, real-world solution for fake media detection in social platforms and biometric verification systems.
Journal Article
Deep Learning for Medical Image Cryptography: A Comprehensive Review
Electronic health records (EHRs) security is a critical challenge in the implementation and administration of Internet of Medical Things (IoMT) systems within the healthcare sector’s heterogeneous environment. As digital transformation continues to advance, ensuring privacy, integrity, and availability of EHRs become increasingly complex. Various imaging modalities, including PET, MRI, ultrasonography, CT, and X-ray imaging, play vital roles in medical diagnosis, allowing healthcare professionals to visualize and assess the internal structures, functions, and abnormalities within the human body. These diagnostic images are typically stored, shared, and processed for various purposes, including segmentation, feature selection, and image denoising. Cryptography techniques offer a promising solution for protecting sensitive medical image data during storage and transmission. Deep learning has the potential to revolutionize cryptography techniques for securing medical images. This paper explores the application of deep learning techniques in medical image cryptography, aiming to enhance the privacy and security of healthcare data. It investigates the use of deep learning models for image encryption, image resolution enhancement, detection and classification, encrypted compression, key generation, and end-to-end encryption. Finally, we provide insights into the current research challenges and promising directions for future research in the field of deep learning applications in medical image cryptography.
Journal Article
A reversible watermarking for image content authentication based on wavelet transform
In the special field of anti-counterfeit authentication, such as medical archive protection, there is a problem of copyright infringement. To protect the privacy contents and achieve lossless recovery, we propose a reversible watermarking technology. In order to solve the problem, a wavelet transform-based reversible watermarking algorithm is proposed for image content authentication. To avoid the influence of tampering on the wavelet domain of the whole image, each block is transformed by wavelet transform as the carrier signal for carrying the watermark information. In the aspect of security, two watermarks are encrypted and scrambled by chaotic mapping with different keys, which increases the security of the watermark and the randomness of embedding. In tamper detection, different levels of detection and screening are used to improve the accuracy. When restoring the tampered area, two watermarks are used to reconstruct the category and feature information of the image block. Experimental results show that, after general tampering, constant mean tampering and paste tampering, the proposed watermarking algorithm can accurately detect and locate tampered areas and can restore the approximate original image information with high quality.
Journal Article
High Capacity and Reversible Fragile Watermarking Method for Medical Image Authentication and Patient Data Hiding
The exchange of medical images and patient data over the internet has attracted considerable attention in the past decade, driven by advancements in communication and health services. However, transferring confidential data through insecure channels, such as the internet, exposes it to potential manipulations and attacks. To ensure the authenticity of medical images while concealing patient data within them, this paper introduces a high-capacity and reversible fragile watermarking model in which an authentication watermark is initially generated from the cover image and merged with the patient’s information, photo, and medical report to form the global watermark. This watermark is subsequently encrypted using the chaotic Chen system technique, enhancing the model’s security and ensuring patient data confidentiality. The cover image then undergoes a Discrete Fourier Transform (DFT) and the encrypted watermark is inserted into the frequency coefficients using a new embedding technique. The experimental results demonstrate that the proposed method achieves great watermarked image quality, with a PSNR exceeding 113 dB and an SSIM close to 1, while maintaining a high embedding capacity of 3 BPP (Bits Per Pixel) and offering perfect reversibility. Furthermore, the proposed model demonstrates high sensitivity to attacks, successfully detecting tampering in all 18 tested attacks, and achieves nearly perfect watermark extraction accuracy, with a Bit Error Rate (BER) of 0.0004%. This high watermark extraction accuracy is crucial in our situation where patient data need to be retrieved from the watermarked images with almost no alteration.
Journal Article