Reliable Mark-Embedded Algorithm for Verifying Archived/Encrypted Image Contents in Presence Different Attacks with FEC Utilizing Consideration

Wireless Personal Communications - Due to the widely spreading of fake news utilizing image manipulation and its bad effects, this paper investigates efficient image contents verification and...     

Efficient video watermarking based on singular value decomposition in the discrete wavelet transform domain

This paper presents an efficient, robust, and imperceptible video watermarking technique based on singular value decomposition (SVD) performed in the Discrete Wavelet Transform (DWT) domain. In the proposed DWT-based SVD video watermarking method, the video frames are transformed with the DWT using two resolution levels. The high frequency band HH and the middle frequency bands LH and HL are SVD transformed and the watermark is hidden in them. The proposed DWT-based SVD video watermarking method is characterized by two improvements: (1) a cascade of two powerful mathematical transforms; the Discrete Wavelet Transform (DWT)-based SVD using additive method, and (2) an error correction code is applied and embeds the watermark with spatial and temporal redundancy. The aim of these improvements is to increase robustness against attacks based on video characteristics and the robustness against image processing attacks, realize high security level, protect the watermark against bit errors and obtain good perceptual quality. The proposed DWT-based SVD video watermarking method has been tested in the presence of video and image processing attacks and experimental results proved that the proposed DWT-based SVD video watermarking method survives attacks based on video characteristics and image processing techniques.     

A novel deep learning framework for copy-moveforgery detection in images

Multimedia Tools and Applications - 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...     

Efficient audio integrity verification algorithm using discrete cosine transform

Audio recordings have been used as evidence for long times. Multimedia processing advancement makes it difficult to be completely sure about what is heard is the truth. This paper presents a promising approach for integrity verification of recorded audio signals using discrete cosine transform. This approach is based on self embedding concept which embeds block-based marks extracted from the same audio signal after being transformed into 2-D format into other blocks according to a specific algorithm. After the self-embedding process, the data is converted back into 1-D style which represents a marked audio signal. The 1-D audio signal is converted into a 2-D format and then converted back into a 1-D format using the popular lexicographic ordering scheme utilized in image processing. Reverse processes are executed to extract the verification marks from the audio signal throughout the integrity verification process. Based on the extracted audio signal properties, the integrity of the marked audio signal is evaluated. Different audio processing tasks and attacks are implemented to examine the suitability of the proposed algorithm for verifying the integrity of high-confidentiality recorded audio data. The results show that the efficient ability of the proposed approach to verify integrity and detect attacks.     

Brain Tumor Auto-Segmentation on Multimodal Imaging Modalities Using Deep Neural Network

Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional Magnetic Resonance Image (MRI) and Computed Tomography (CT) scans utilizing 3D U-Net Design and ResNet50, taken after by conventional classification strategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, and the 3D U-Net scored 97.99% among the different methods of deep learning. It is to be mentioned that traditional Convolutional Neural Network (CNN) gives 97.90% accuracy on top of the 3D MRI. In expansion, the image fusion approach combines the multimodal images and makes a fused image to extricate more highlights from the medical images. Other than that, we have identified the loss function by utilizing several dice measurements approach and received Dice Result on top of a specific test case. The average mean score of dice coefficient and soft dice loss for three test cases was 0.0980. At the same time, for two test cases, the sensitivity and specification were recorded to be 0.0211 and 0.5867 using patch level predictions. On the other hand, a software integration pipeline was integrated to deploy the concentrated model into the webserver for accessing it from the software system using the Representational state transfer (REST) API. Eventually, the suggested models were validated through the Area Under the Curve–Receiver Characteristic Operator (AUC–ROC) curve and Confusion Matrix and compared with the existing research articles to understand the underlying problem. Through Comparative Analysis, we have extracted meaningful insights regarding brain tumour segmentation and figured out potential gaps. Nevertheless, the proposed model can be adjustable in daily life and the healthcare domain to identify the infected regions and cancer of the brain through various imaging modalities.     

An enhanced chaotic key‐based RC5 block cipher adapted to image encryption

RC5 is a block cipher that has several salient features such as adaptability to process different word lengths with a variable block size, a variable number of rounds and a variable‐length secret key. However, RC5 can be broken with various attacks such as correlation attack, timing attack, known plaintext correlation attack and differential attacks, revealing weak security. We aimed to enhance the RC5 block cipher to be more secure and efficient for real‐time applications while preserving its advantages. For this purpose, this article introduces a new approach based on strengthening both the confusion and diffusion operations by combining chaos and cryptographic primitive operations to produce round keys with better pseudo‐random sequences. Comparative security analysis and performance evaluation of the enhanced RC5 block cipher (ERC5) with RC5, RC6 and chaotic block cipher algorithm (CBCA) are addressed. Several test images are used for inspecting the validity of the encryption and decryption algorithms. The experimental results show the superiority of the suggested enhanced RC5 (ERC5) block cipher to image encryption algorithms such as RC5, RC6 and CBCA from the security analysis and performance evaluation points of view.     

A fast hybrid image cryptosystem based on random generator and modified logistic map

Multimedia Tools and Applications - This paper demonstrates an image encryption technique using a hybrid method. This method consists of two stages. The image is XORed with the Pseudo-Random Number...