Haitao Zhang^*

Journal of Information Hiding and Privacy Protection, Vol.4, No.1, pp. 41-51, 2022, DOI:10.32604/jihpp.2022.031707

Abstract Deep learning related technologies, especially generative adversarial network, are widely used in the fields of face image tampering and forgery. Forensics researchers have proposed a variety of passive forensic and related anti-forensic methods for image tampering and forgery, especially face images, but there is still a lack of overview of anti-forensic methods at this stage. Therefore, this paper will systematically discuss the anti-forensic methods for face image tampering and forgery. Firstly, this paper expounds the relevant background, including the relevant tampering and forgery methods and forensic schemes of face images. The former mainly includes four aspects: conventional processing, fake face… More >

Aiming Wu¹, Shanshan Tu^1,*, Muhammad Wagas^1,2,3, Yongjie Yang¹, Yihe Zhang¹, Xuetao Bai¹

Journal of Information Hiding and Privacy Protection, Vol.4, No.1, pp. 25-39, 2022, DOI:10.32604/jihpp.2022.029922

Abstract With the rapid development of the Internet of Things (IoT), all kinds of data are increasing exponentially. Data storage and computing on cloud servers are increasingly restricted by hardware. This has prompted the development of fog computing. Fog computing is to place the calculation and storage of data at the edge of the network, so that the entire Internet of Things system can run more efficiently. The main function of fog computing is to reduce the burden of cloud servers. By placing fog nodes in the IoT network, the data in the IoT devices can be transferred to the fog… More >

Kai Chen^*, Jinwei Wang, Jiawei Zhang

Journal of Information Hiding and Privacy Protection, Vol.4, No.1, pp. 15-24, 2022, DOI:10.32604/jihpp.2022.029006

Abstract In recent years, machine learning has become more and more popular, especially the continuous development of deep learning technology, which has brought great revolutions to many fields. In tasks such as image classification, natural language processing, information hiding, multimedia synthesis, and so on, the performance of deep learning has far exceeded the traditional algorithms. However, researchers found that although deep learning can train an accurate model through a large amount of data to complete various tasks, the model is vulnerable to the example which is modified artificially. This technology is called adversarial attacks, while the examples are called adversarial examples.… More >

Zhiguo Hong^1,*, Yongbin Wang^2,3, Minyong Shi⁴

Journal of Information Hiding and Privacy Protection, Vol.4, No.1, pp. 1-13, 2022, DOI:10.32604/jihpp.2022.026440

Abstract With the rapid development of various applications of Information Technology, big data are increasingly generated by social network services (SNS) nowadays. The designers and providers of SNS distribute different client applications for PC, Mobile phone, IPTV etc., so that users can obtain related service via mobile or traditional Internet. Good scalability and considerably short time delay are important indices for evaluating social network systems. As a result, investigating and mining the principle of users’ behaviors is an important issue which can guide service providers to establish optimal systems with SNS. On the basis of analyzing the characteristics of social network… More >

Abdallah Abdallah¹, Mohammed Dauwed², Ayman A. Aly³, Bassem F. Felemban³, Imran Khan⁴, Bong Jun Choi^5,*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4311-4327, 2022, DOI:10.32604/cmc.2022.031514

Abstract From raw material storage through final product distribution, a cold supply chain is a technique in which all activities are managed by temperature. The expansion in the number of imported meat and other comparable commodities, as well as exported seafood has boosted the performance of cold chain logistics service providers. On the basis of the standard basic-pursuit (BP) neural network, a rough BP particle swarm optimization (PSO) neural network model is constructed by combining rough set and particle swarm algorithms to aid cold chain food production enterprises in quickly picking the best cold chain logistics service providers. To reduce duplicate… More >

Nagwan Abdel Samee¹, El-Sayed M. El-Kenawy^2,3, Ghada Atteia^1,*, Mona M. Jamjoom⁴, Abdelhameed Ibrahim⁵, Abdelaziz A. Abdelhamid^6,7, Noha E. El-Attar⁸, Tarek Gaber^9,10, Adam Slowik¹¹, Mahmoud Y. Shams¹²

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4193-4210, 2022, DOI:10.32604/cmc.2022.031147

Abstract As corona virus disease (COVID-19) is still an ongoing global outbreak, countries around the world continue to take precautions and measures to control the spread of the pandemic. Because of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals, a rapid, reliable, and automatic detection of COVID-19 is in extreme need to curb the number of infections. By analyzing the COVID-19 chest X-ray images, a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm optimizers. The lung region was segmented from the original chest X-ray images and augmented using various… More >

Vasumathi Devi Majety¹, N. Sharmili², Chinmaya Ranjan Pattanaik³, E. Laxmi Lydia⁴, Subhi R. M. Zeebaree⁵, Sarmad Nozad Mahmood⁶, Ali S. Abosinnee⁷, Ahmed Alkhayyat^8,*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4393-4406, 2022, DOI:10.32604/cmc.2022.031109

Abstract Histopathology is the investigation of tissues to identify the symptom of abnormality. The histopathological procedure comprises gathering samples of cells/tissues, setting them on the microscopic slides, and staining them. The investigation of the histopathological image is a problematic and laborious process that necessitates the expert’s knowledge. At the same time, deep learning (DL) techniques are able to derive features, extract data, and learn advanced abstract data representation. With this view, this paper presents an ensemble of handcrafted with deep learning enabled histopathological image classification (EHCDL-HIC) model. The proposed EHCDL-HIC technique initially performs Weiner filtering based noise removal technique. Once the… More >

Wajdi Elhamzi^1,2,*, Malika Jallouli³, Yassine Bouteraa^1,4

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4329-4347, 2022, DOI:10.32604/cmc.2022.030954

Abstract Since 3D mesh security has become intellectual property, 3D watermarking algorithms have continued to appear to secure 3D meshes shared by remote users and saved in distant multimedia databases. The novelty of our approach is that it uses a new Clifford-multiwavelet transform to insert copyright data in a multiresolution domain, allowing us to greatly expand the size of the watermark. After that, our method does two rounds of insertion, each applying a different type of Clifford-wavelet transform. Before being placed into the Clifford-multiwavelet coefficients, the watermark, which is a mixture of the mesh description, source mesh signature (produced using SHA512),… More >

Moheb R. Girgis, Rofida M. Gamal, Enas Elgeldawi^*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3951-3967, 2022, DOI:10.32604/cmc.2022.030934

Abstract Protein structure prediction is one of the most essential objectives practiced by theoretical chemistry and bioinformatics as it is of a vital importance in medicine, biotechnology and more. Protein secondary structure prediction (PSSP) has a significant role in the prediction of protein tertiary structure, as it bridges the gap between the protein primary sequences and tertiary structure prediction. Protein secondary structures are classified into two categories: 3-state category and 8-state category. Predicting the 3 states and the 8 states of secondary structures from protein sequences are called the Q3 prediction and the Q8 prediction problems, respectively. The 8 classes of… More >

Abdullah A. Asiri¹, Tariq Ali², Ahmad Shaf², Muhammad Aamir², Muhammad Shoaib³, Muhammad Irfan⁴, Hassan A. Alshamrani^1,*, Fawaz F. Alqahtani¹, Osama M. Alshehri⁵

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3983-4002, 2022, DOI:10.32604/cmc.2022.030923

Abstract Brain tumor is one of the most dreadful worldwide types of cancer and affects people leading to death. Magnetic resonance imaging methods capture skull images that contain healthy and affected tissue. Radiologists checked the affected tissue in the slice-by-slice manner, which was time-consuming and hectic task. Therefore, auto segmentation of the affected part is needed to facilitate radiologists. Therefore, we have considered a hybrid model that inherits the convolutional neural network (CNN) properties to the support vector machine (SVM) for the auto-segmented brain tumor region. The CNN model is initially used to detect brain tumors, while SVM is integrated to… More >