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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Rohitha Chowdary, G. Tharun, J. Sivaiah, G. Raveena, S. Surendran, R. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Insititute of Medical and Technical Science Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

In the rapidly evolving cosmetics market, it is imperative to prioritize customer safety while offering personalized recommendations. This study introduces a hybrid recommendation system for cosmetics, using deep learning to combine the advantages of collaborative filtering with content-based filtering. The system assesses user preferences, skin types, and ingredient sensitivities, recommending solutions that are both effective and safe. The system proposes an integration of user-based and item-based collaborative filtering methodologies with deep neural networks to effectively elucidate intricate links among individuals, items, and their characteristics. This hybrid technique facilitates the system's ability to propose goods that provide both aesthetic appeal and skin safety, therefore fostering a healthier and more confident lifestyle. The system has an impressive accuracy of 92.5% in recommending suitable cosmetic products for individual clients. This enhanced accuracy reduces the likelihood of negative reactions and elevates customer satisfaction via personalized suggestions. © 2025 IEEE.

关键词： Contrastive Learning

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Attention-based network embedding with higher-order weights and node attributes

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 440-451页

作者： Xian Mo Binyuan Wan Rui Tang Junkai Ding Guangdi Liu School of Information Engineering Ningxia UniversityYinchuanChina School of Cyber Science and Engineering Sichuan UniversityChengduChina Computer Science and Information Technology University of MalayaKuala LumpurMalaysia Academy of Medical Sciences Zhengzhou UniversityZhengzhouChina

Network embedding aspires to learn a low-dimensional vector of each node in networks,which can apply to diverse data mining *** real-life,many networks include rich attributes and temporal ***,most existing embedding approaches ignore either temporal information or network attributes.A self-attention based architecture using higher-order weights and node attributes for both static and temporal attributed network embedding is presented in this article.A random walk sampling algorithm based on higher-order weights and node attributes to capture network topological features is *** static attributed networks,the algorithm incorporates first-order to k-order weights,and node attribute similarities into one weighted graph to preserve topological features of *** temporal attribute networks,the algorithm incorporates previous snapshots of networks containing first-order to k-order weights,and nodes attribute similarities into one weighted *** addition,the algorithm utilises a damping factor to ensure that the more recent snapshots allocate a greater *** features are then incorporated into topological ***,the authors adopt the most advanced architecture,Self-Attention Networks,to learn node *** results on node classification of static attributed networks and link prediction of temporal attributed networks reveal that our proposed approach is competitive against diverse state-of-the-art baseline approaches.

关键词： data mining deep neural networks social network

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A Neural Computing-Based Access Control Protocol for AI-Driven Intelligent Flying Vehicles in Industry 5.0-Assisted Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 3573-3581页

作者： Mahmood, Khalid Tariq, Tayyaba Sangaiah, Arun Kumar Ghaffar, Zahid Saleem, Muhammad Asad Shamshad, Salman National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology International Graduate School of AI Douliu64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102 2801 Lebanon University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China The University of Lahore Department of Software Engineering Lahore54590 Pakistan

The continuous revolution in Artificial Intelligence (AI) has played a significant role in the development of key consumer applications, including Industry 5.0, autonomous decision-making, fault diagnosis, etc. In practice, autonomous and intelligent robots and flying vehicles (i.e., drones) are also becoming the integral part of Industry 5.0. These vehicles are attaining significant importance nowadays because of their applications in consumer electronics. However, there may be numerous privacy issues in such an infrastructure. Nevertheless, neural computing-driven security solutions have played a significant role in overcoming the security and privacy issues of AI-driven intelligent flying vehicles. Motivated by these facts, this article presents a neural computing-based access control protocol for AI-driven flying vehicles to prevent all possible security threats. A detailed security evaluation of the devised protocol is done formally using the extensively recognized Real-Or-Random (ROR) oracle model. Similarly, the informal security analysis ensures the resistance of our scheme against various potential attacks under the assumptions of CK and DY adversarial models. Moreover, detailed performance scrutiny of the devised protocol with various existing schemes shows the robustness and supremacy of our scheme in terms of computation, communication overhead, and security attributes. © 1975-2011 IEEE.

关键词： Authentication

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A General Linguistic Steganalysis Framework Using Multi-Task Learning

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computer Systems science & engineering 2023年第8期46卷 2383-2399页

作者： Lingyun Xiang Rong Wang Yuhang Liu Yangfan Liu Lina Tan School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer Science and Electronic Engineering University of EssexColchesterCO43SQUK School of Computer Science Hunan University of Technology and BusinessChangsha410205China

Prevailing linguistic steganalysis approaches focus on learning sensitive features to distinguish a particular category of steganographic texts from non-steganographic texts,by performing binary *** it remains an unsolved problem and poses a significant threat to the security of cyberspace when various categories of non-steganographic or steganographic texts *** this paper,we propose a general linguistic steganalysis framework named LS-MTL,which introduces the idea of multi-task learning to deal with the classification of various categories of steganographic and non-steganographic ***-MTL captures sensitive linguistic features from multiple related linguistic steganalysis tasks and can concurrently handle diverse tasks with a constructed *** the proposed framework,convolutional neural networks(CNNs)are utilized as private base models to extract sensitive features for each steganalysis ***,a shared CNN is built to capture potential interaction information and share linguistic features among all ***,LS-MTL incorporates the private and shared sensitive features to identify the detected text as steganographic or *** results demonstrate that the proposed framework LS-MTL outperforms the baseline in the multi-category linguistic steganalysis task,while average Acc,Pre,and Rec are increased by 0.5%,1.4%,and 0.4%,*** ablation experimental results show that LS-MTL with the shared module has robust generalization capability and achieves good detection performance even in the case of spare data.

关键词： Linguistic steganalysis multi-task learning convolutional neural network(CNN) feature extraction detection performance

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Privacy-preserving Image Inpainting Using Markov Random Field Modeling

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IEEE Transactions on Multimedia 2025年 27卷 3688-3701页

作者： Kong, Ping Li, An Guo, Daidou Zhou, Liang Qin, Chuan Zhang, Xinpeng Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences Shanghai201800 China University of Shanghai for Science and Technology School of Health Science and Engineering Shanghai200093 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Fudan University School of Computer Science Shanghai200433 China

Cloud services have attracted extensive attention due to low cost, agility and mobility. However, when processing data on cloud servers, users may worry about semi-honest third parties stealing private information from them, hence, data encryption is applied for privacy protection. Inpainting is a technique that reconstructs certain undesirable regions in an image through an imperceptible manner, which can be accomplished by searching for well-matching candidate patches and copying them to to-be-inpainted locations. However, when the image is encrypted, the matched candidate patch searching is a challenging dilemma. Therefore, tackling these data-privacy issues for image inpainting over a cloud infrastructure, we propose an image inpainting scheme using Markov random field (MRF) modeling in encrypted domain. In this scheme, the sender encrypts the to-be-inapinted image by using a homomorphic cryptosystem that supports homomorphic ciphertext comparison. Then, the cloud realizes the MRF-based inpainting for encrypted images through some specific homomorphic operations. In addition, secure context descriptors are utilized to improve the inpainting of textures and structures. Finally, the receiver obtains the inpainted result through image decryption. The proposed scheme is proved to be secure through various cryptographic attacks. Qualitative and quantitative results demonstrate our scheme achieves better inpainted results in structure compared with state-of-the-art schemes in encrypted domain. © 1999-2012 IEEE.

关键词： Differential privacy

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Impact of the chaotic semiconductor laser output power on time-delay-signature of chaos and random bit generation

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Chinese Physics B 2025年第3期34卷 325-330页

作者： Chenpeng Xue Xu Wang Likai Zheng Haoyu Zhang Yanhua Hong Zuxing Zhang College of Electronic and Optical Engineering and College of Flexible Electronics(Future Technology) Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Computer Science and Engineering Bangor UniversityBangor LL571UTUK

We experimentally analyze the effect of the optical power on the time delay signature identification and the random bit generation in chaotic semiconductor laser with optical *** to the inevitable noise during the photoelectric detection and analog-digital conversion,the varying of output optical power would change the signal to noise ratio,then impact time delay signature identification and the random bit *** results show that,when the optical power is less than-14 dBm,with the decreasing of the optical power,the actual identified time delay signature degrades and the entropy of the chaotic signal ***,the extracted random bit sequence with lower optical power is more easily pass through the randomness testing.

关键词： chaos time delay signature entropy estimation random bit generation

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Detecting Inaudible Voice Commands via Acoustic Attenuation by Multi-channel Microphones

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IEEE Transactions on Dependable and Secure Computing 2024年 1-17页

作者： Ji, Xiaoyu Zhang, Guoming Li, Xinfeng Qu, Gang Cheng, Xiuzhen Xu, Wenyuan Electrical Engineering of Zhejiang University Hangzhou China School of Computer Science and Technology of Shandong University Qingdao China Department of Electrical and Computer Engineering at the University of Maryland College Park MD USA

DolphinAttacks (i.e., inaudible voice commands) modulate audible voices over ultrasounds to inject malicious commands silently into voice assistants and manipulate controlled systems (e.g., doors or smart speakers). Eliminating DolphinAttacks is challenging if ever possible since it requires to modify the microphone hardware. In this paper, we design EarArray, a lightweight method that can not only detect such attacks but also identify the direction of attackers without requiring any extra hardware or hardware modification. Essentially, inaudible voice commands are modulated on ultrasounds that inherently attenuate faster than the one of audible sounds. By inspecting the command sound signals via the built-in multiple microphones on smart devices, EarArray is able to estimate the attenuation rate and thus detect the attacks. We propose a model of the propagation of audible sounds and ultrasounds from the sound source to a voice assistant, e.g., a smart speaker, and illustrate the underlying principle and its feasibility. We implemented EarArray using two specially-designed microphone arrays and our experiments show that EarArray can detect inaudible voice commands with an accuracy of above 99% and recognize the direction of the attackers with an accuracy of 97.89% and can also detect the laser-based attack with an accuracy of 100%. IEEE

关键词： Microphones

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A new multimodal sentiment analysis for images containing textual information

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Multimedia Tools and Applications 2024年 1-30页

作者： Ahuja, Garvit Alaei, Alireza Pal, Umapada School of Computer Science and Engineering Manipal University Jaipur Jaipur India Faculty of Science and Engineering Southern Cross University Gold Coast Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Multimodal sentiment analysis on images with textual content is a research area aiming to understand the sentiment conveyed by visual and textual elements in the images. While multimodal sentiment analysis on images and text (reviews) has its own challenges, the combination of textual and visual content in the form of images presents new challenges as well as opportunities. In this research work, we proposed a multimodal sentiment analysis method that works on images incorporating textual elements. In the textual sentiment analysis model, we initially employed a recognition system to extract textual data from input images. Our proposed multimodal method is based on transfer learning, considering two pre-trained deep learning models, Xception, and RoBERTa, to extract features from both visual and textual content from multimedia images. We then implemented a fusion strategy to combine these two modalities (Visual Sentiment Analysis (VSA) and Textual Sentiment Analysis (TSA)) to enhance the accuracy of the proposed method and to provide a more comprehensive understanding of sentiment in multimedia content. In addition, we curated a custom dataset comprising images with associated text labels and sentiments. To ensure accurate labels, we conducted human evaluations involving thirty annotators. Our dataset includes images labeled with negative, neutral, and positive sentiments. Experimental results demonstrated the effectiveness of combining visual and textual features for sentiment analysis. The findings from this research hold promising implications for real-world applications, such as sentiment analysis in social media, product reviews, and marketing campaigns, where both images and text play a significant role in conveying emotional context. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Contrastive Learning

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ScanSavant: Malware Detection for Android Applications with Explainable AI

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第19期18卷 171-181页

作者： Navaneethan, S. Udhaya Kumar, S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India

Mobile devices face SQL injection, malware, and web-based threats. Current solutions lack real-time detection. This paper introduces an Android app with advanced algorithms for real-time threat scanning. During testing, our application detected 94% of SQL injection attempts, outperforming the 86% average detection rate in similar studies. For malware analysis, it achieved a 97% detection accuracy on a dataset of infected files, higher than the industry standard of 93%. Additionally, our app can detect 85 malware variants and assign 15 attributes (***.8, ***, ***, ***, ***, ***, ***-2017-0143, ***, ***, Backdoor. DarkComet, ***, ***, ***, ***32. Necurs, and ***) to some malwares, providing detailed analysis for better threat management. The application effectively scans both EXE and APK files, ensuring com-prehensive protection. When assessing website links, the application identified security risks with 96% accuracy, demonstrating its capability in managing web-based threats. This app detects SQL injections, analyses malware, and assesses website security, bolstering cyber defence with user-friendly features and top-notch threat mitigation. © 2024, International Federation of engineering Education Societies (IFEES). All rights reserved.

关键词： Android malware

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Deep Fake Analyser: A Review Based on Detecting the Deepfakes 1

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3rd International Conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Srivastava, Santosh Kumar Srivastava, Durgesh Kantha, Praveen Mahto, Manoj Kumar Kumar, Sheo Singh, Hare Ram Department of Computer Science and Engineering School of Computing Science and Engineering and Technology Galgotias University Greater Noida India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Computer Science and Engineering School of Engineering and Technology Chitkara University Baddi Himachal Pradesh Baddi India Department of Computer Science and Engineering Vignan Institute of Technology and Science Hyderabad India Department of Computer Science and Engineering CMR Engineering College Hyderabad India Department of Computer Science and Engineering GNIOT Greater Noida India

ISBN: (数字)9789819725083

ISBN: (纸本)9789819725076

With the speedy growth in the technology and automation sectors, different techniques have been developed which can easily manipulate multimedia content such as videos and images with the ultimate level of realism. It becomes very hard for a person to find out which of the media content is real and which is fake. With the help of deep learning and GANs (Generative Adversarial Networks), deep fakes can be produced. A Deep fake is a fake video or image where a person in an existing video or image is replaced by someone else by using an algorithm. If we look at the positives, we can see how deep fake technology can be used in advertisements, filmmaking, and video games. But on the contrary, it also results in the spread of fake news and the generation of pornographic content. This seriously impacts society and presents enormous threats. This research paper aims to present a paper based on a deep fake analyzer that will be able to identify and recognize fake media with the help of deep neural networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Generative adversarial networks

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