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State-of-the-art AI-based Learning Approaches for Deepfake Generation and Detection, Analyzing Opportunities, Threading through Pros, Cons, and Future Prospects

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arXiv 2025年

作者： Goyal, Harshika Wajid, Mohammad Saif Wajid, Mohd Anas Khanday, Akib Mohi Ud Din Neshat, Mehdi Gandomi, Amir Indian Institute of Technology Kharagpur India Department of Computer Science and Intelligent Systems Tecnológico de Monterrey Monterrey Mexico Institute for the Future of Education TEC de Monterrey Monterrey Mexico Department of Computer Science and Software Engineering United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Department of Computer Science Samarkand International University of Technology Samarkand Uzbekistan Faculty of Engineering and Information Technology University of Technology Sydney Ultimo SydneyNSW2007 Australia Obuda University Budapest1034 Hungary School of Computer and Mathematical Sciences University of Adelaide Adelaide5005 Australia

The rapid expansion and advancement of deepfake technology, which is specifically designed to create incredibly lifelike facial imagery and video content, has ignited a remarkable level of interest and curiosity across a multitude of fields, including but not limited to the realms of the entertainment industry, forensic analysis, cybersecurity and the innovative creation of digital characters. By harnessing the latest breakthroughs in deep learning methodologies, especially through the implementation of techniques such as Generative Adversarial Networks, Variational Autoencoders, Few-Shot Learning Strategies, and Transformer Neural Networks, the outcomes achieved in the generation of deepfakes have been nothing short of astounding and transformative. Furthermore, the recent advancements in diffusion models have served to invigorate and catalyze ongoing research efforts in this fascinating domain. At the same time, the ongoing evolution of detection technologies is being developed to counteract the potential for misuse associated with deepfakes, effectively addressing critical concerns that range from political manipulation to the dissemination of fake news and the ever-growing issue of cyberbullying. This comprehensive review paper meticulously investigates the most recent developments in both deepfake generation and detection, including around 400 publications, providing an in-depth analysis of the cutting-edge innovations that are shaping this rapidly evolving landscape. Starting with a thorough examination of systematic literature review methodologies, we embark on a journey that delves into the complex technical intricacies inherent in the various techniques used for deepfake generation, comprehensively addressing the challenges faced, potential solutions available, and the nuanced details surrounding manipulation formulations. Besides, task definitions are standardized to ensure clarity, while datasets and metrics are diligently introduced, paving the way for a

关键词： Generative adversarial networks

Drug repositioning for Alzheimer's disease with transfer learning

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arXiv 2022年

作者： Wu, Yetao Liu, Han Yan, Jie Hu, Xiaolin Department of Computer Science and Technology Institute for Artificial Intelligence State Key Laboratory of Intelligent Technology and Systems BNRist THBI Tsinghua University Beijing China

Deep Learning and DRUG-seq (Digital RNA with perturbation of genes) have attracted attention in drug discovery. However, the public DRUG-seq dataset is too small to be used for directly training a deep learning neural network from scratch. Inspired by the transfer learning technique, we pretrain a drug efficacy prediction neural network model with the Library of Integrated Network-based Cell-Signature (LINCS) L1000 data and then use human neural cell DRUG-seq data to fine-tune it. After training, the model is used for virtual screening to find potential drugs for Alzheimer’s disease (AD) treatment. Finally, we find 27 potential drugs for AD treatment including Irsogladine (PDE4 inhibitor), Tasquinimod (HDAC4 selective inhibitor), Suprofen (dual COX-1/COX-2 inhibitor) et al. Copyright © 2022, The Authors. All rights reserved.

关键词： Diagnosis

A Comparative Study on the Efficiency of the Modified COMET in Decision-Making

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Procedia computer science 2024年 246卷 103-112页

作者： Bartosz Paradowski Paweł Olender Wojciech Sałabun Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Żołnierska 49 71-210 Szczecin Poland National Institute of Telecommunications ul. Szachowa 1 04-894 Warsaw Poland

Decision-making is a high-risk area and, simultaneously, a process that cannot be ignored. Risks often arise under challenging situations where ranking the options under consideration is a complex problem. For such purposes, multi-criteria decision-making methods are resorted to, which, despite continuous improvement, still have some weaknesses, often due to their characteristics. One such method that has seen many modifications in the literature is the Characteristic Objects METhod (COMET). It allows a simplified comparison of alternatives considered in a given problem, compared to the Analytic Hierarchy Process (AHP), but has a relatively high computational complexity. In this study, we present a modification of the COMET method that simplifies the procedure for assigning preference values to characteristic objects. Furthermore, a comparison with the classical approach is given regarding the obtained preference values, rankings, and criteria weights, which occur implicitly in this method. The study showed that the proposed modification has grounds for broader use and offers similar results to classical COMET while reducing the number of required operations to obtain preference values.

关键词： COMET analysis MCDA MCDM weights decision making

Comparative sensitivity analysis of single and multiple modifications in multi-criteria decision analysis

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Procedia computer science 2024年 246卷 91-102页

作者： Jakub Więckowski Paweł Białoń Wojciech Sałabun Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Żołnierska 49 71-210 Szczecin Poland National Institute of Telecommunications ul. Szachowa 1 04-894 Warsaw Poland

In the area of decision-making, where complexities often exceed experts’ analytical capabilities, this study addresses a critical gap by introducing a novel methodology for sensitivity analysis within the Multi-Criteria Decision Analysis (MCDA) problems. Current analytical frameworks struggle to comprehensively consider potential modifications to values within decision matrices, making it challenging to understand their impact in detail. To address this challenge, decision-makers need enhanced tools, and MCDA methods combined with systematic changes in selected elements of the decision matrix stand out as a promising approach. However, existing studies primarily focus on different criteria-weight scenarios, leaving an unexplored gap in the simultaneous modification of multiple values within the decision matrix. This paper integrates sensitivity analysis within the MCDA methods, extending its role in the comprehensive assessment of decision problems. Sensitivity analysis becomes crucial in offering decision-makers a broader perspective, aiding them in navigating the complexities of decision-making in dynamic environments. Recognizing the unexplored potential in sensitivity analysis, the study proposed a novel approach of simultaneous modification of multiple values in a decision matrix, offering an extension of conventional one-at-a-time modifications. As a Proof of Concept (PoC) research work, the study investigated whether the determined approach provides divergent preference scores compared to the traditional single-modification method across ten different MCDA techniques. The results showed that modifying multiple values simultaneously produced different preference scores of alternatives than in the case of a conventional single change, showing that additional insight knowledge could be extracted from this type of sensitivity analysis.

关键词： Sensitivity analysis Multiple changes Decision making Results robustness MCDA

Create Your World: Lifelong Text-to-Image Diffusion

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arXiv 2023年

作者： Sun, Gan Liang, Wenqi Dong, Jiahua Li, Jun Ding, Zhengming Cong, Yang State Key Laboratory of Robotics Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China The School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu210094 China The Department of Computer Science Tulane University New OrleansLA70118 United States The College of Automation Science and Engineering South China University of Technology Guangzhou510640 China University of Chinese Academy of Sciences Beijing100049 China

Text-to-image generative models can produce diverse high-quality images of concepts with a text prompt, which have demonstrated excellent ability in image generation, image translation, etc. We in this work study the problem of synthesizing instantiations of a user's own concepts in a never-ending manner, i.e., create your world, where the new concepts from user are quickly learned with a few examples. To achieve this goal, we propose a Lifelong text-to-image Diffusion Model (L2DM), which intends to overcome knowledge "catastrophic forgetting" for the past encountered concepts, and semantic "catastrophic neglecting" for one or more concepts in the text prompt. In respect of knowledge "catastrophic forgetting", our L2DM framework devises a task-aware memory enhancement module and a elastic-concept distillation module, which could respectively safeguard the knowledge of both prior concepts and each past personalized concept. When generating images with a user text prompt, the solution to semantic "catastrophic neglecting" is that a concept attention artist module can alleviate the semantic neglecting from concept aspect, and an orthogonal attention module can reduce the semantic binding from attribute aspect. To the end, our model can generate more faithful image across a range of continual text prompts in terms of both qualitative and quantitative metrics, when comparing with the related state-of-the-art models. The code will be released at https://***/. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

Diffusion Based Causal Representation Learning

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arXiv 2023年

作者： Mamaghan, Amir Mohammad Karimi Dittadi, Andrea Bauer, Stefan Johansson, Karl Henrik Quinzan, Francesco KTH Royal Institute of Technology Sweden Helmholtz AI Helmholtz Center Munich Germany Max Planck Institute for Intelligent Systems Tübingen Germany Technical University of Munich Germany Department of Computer Science University of Oxford United Kingdom

Causal reasoning can be considered a cornerstone of intelligent systems. Having access to an underlying causal graph comes with the promise of cause-effect estimation and the identification of efficient and safe interventions. However, learning causal representations remains a major challenge, due to the complexity of many real-world systems. Previous works on causal representation learning have mostly focused on Variational Auto-Encoders (VAE). These methods only provide representations from a point estimate, and they are unsuitable to handle high dimensions. To overcome these problems, we proposed a new Diffusion-based Causal Representation Learning (DCRL) algorithm. This algorithm uses diffusion-based representations for causal discovery. DCRL offers access to infinite dimensional latent codes, which encode different levels of information in the latent code. In a first proof of principle, we investigate the use of DCRL for causal representation learning. We further demonstrate experimentally that this approach performs comparably well in identifying the causal structure and causal variables. © 2023, CC BY.

关键词： Diffusion

BinaryAI: Binary Software Composition Analysis via intelligent Binary Source Code Matching

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BinaryAI: Binary Software Composition Analysis via Intellige...

International Conference on Software Engineering (ICSE)

作者： Ling Jiang Junwen An Huihui Huang Qiyi Tang Sen Nie Shi Wu Yuqun Zhang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Shenzhen China Tencent Security Keen Lab Shanghai China Department of Computer Science and Engineering Southern University of Science and Technol-ogy Shenzhen China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

While third-party libraries (TPLs) are extensively reused to enhance productivity during software development, they can also introduce potential security risks such as vulnerability propagation. Software composition analysis (SCA), proposed to identify reused TPLs for reducing such risks, has become an essential procedure within modern DevSecOps. As one of the mainstream SCA techniques, binary-to-source SCA identifies the third-party source projects contained in binary files via binary source code matching, which is a major challenge in reverse engineering since binary and source code exhibit substantial disparities after compilation. The existing binary-to-source SCA techniques leverage basic syntactic features that suffer from redundancy and lack robustness in the large-scale TPL dataset, leading to inevitable false positives and compromised recall. To mitigate these limitations, we introduce BinaryAI, a novel binary-to-source SCA technique with two-phase binary source code matching to capture both syntactic and semantic code features. First, BinaryAI trains a transformer-based model to produce function-level embeddings and obtain similar source functions for each binary function accordingly. Then by applying the link-time locality to facilitate function matching, BinaryAI detects the reused TPLs based on the ratio of matched source functions. Our experimental results demonstrate the superior performance of BinaryAI in terms of binary source code matching and the downstream SCA task. Specifically, our embedding model outperforms the state-of-the-art model CodeCMR, i.e., achieving 22.54% recall@l and 0.34 MRR compared with 10.75% and 0.17 respectively. Additionally, BinaryAI outperforms all existing binary-to-source SCA tools in TPL detection, increasing the precision from 73.36% to 85.84% and recall from 59.81% to 64.98% compared with the well-recognized commercial SCA product Black Duck. E-https://***

关键词： Codes Source coding Semantics Redundancy Syntactics Transformers Software

A New Application of Machine Learning: Detecting Errors in Network Simulations

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A New Application of Machine Learning: Detecting Errors in N...

Simulation Winter Conference

作者： Maciej K. Wozniak Luke Liang Hieu Phan Philippe J. Giabbanelli Department of Intelligent Systems KTH Royal Institute of Technology Stockholm SWEDEN Department of Computer Science & Software Engineering Miami University St. Oxford OH USA

ISBN: (纸本)9781665476621

After designing a simulation and running it locally on a small network instance, the implementation can be scaled-up via parallel and distributed computing (e.g., a cluster) to cope with massive networks. However, implementation changes can create errors (e.g., parallelism errors), which are difficult to identify since the aggregate behavior of an incorrect implementation of a stochastic network simulation can fall within the distributions expected from correct implementations. In this paper, we propose the first approach that applies machine learning to traces of network simulations to detect errors. Our technique transforms simulation traces into images by reordering the network's adjacency matrix, and then training supervised machine learning models. Our evaluation on three simulation models shows that we can easily detect previously encountered types of errors and even confidently detect new errors. This work opens up numerous opportunities by examining other simulation models, representations (i.e., matrix reordering algorithms), or machine learning techniques.

关键词： Training Machine learning algorithms Computational modeling Aggregates Stochastic processes Machine learning Transforms

Knowledge Distillation-Based AIoT Framework for Efficient Wireless Gesture Sensing in B5G/6G Networks

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IEEE Network 2025年

作者： Zeeshan, Mohammad Raj, Rahul Anand, Amit Pandey, Ankur Quasim, Mohammad Tabrez Torres-Sospedra, Joaquin Kumar, Sudhir Dev, Kapal Indian Institute of Technology Department of Electrical Engineering Patna India Rajiv Gandhi Institute of Petroleum Technology Department of Electrical and Electronics Engineering Amethi India University of Bisha College of Computing and Information Technology Department of Computer Science and Artificial Intelligence Bisha Saudi Arabia Universitat de Val‘encia Departament d’Inform‘atica Spain Valencia Spain Munster Technological University Department of Computer Science Bishopstown CorkT12 P928 Ireland University of Johannesburg Department of institute of intelligent systems Auckland Park2006 South Africa Chitkara University Chitkara University Institute of Engineering and Technology Punjab Rajpura140401 India

The advancements in 5G and 6G networks have opened new possibilities for wireless sensing in smart environments. Gesture and activity recognition are crucial for interactive systems, particularly in Internet of Things (IoT) applications, where real-time performance and cost efficiency are critical. This article presents a novel Knowledge Distillation (KD)-based Artificial Intelligence of Things (AIoT) framework that enables efficient, lightweight models for gesture and activity recognition using Wireless Fidelity (Wi-Fi) Channel State Information (CSI). Unlike conventional camera-based or wearable sensor methods, Wi-Fi-based sensing offers a low-cost, privacy-preserving alternative suitable for security-sensitive applications in B5G/6G networks. Experimental results demonstrate that despite its compact structure, the proposed KD-based model achieves accuracy comparable to state-of-the-art methods while being optimized for resource-constrained edge devices. These findings underscore the potential of KD to develop robust, scalable, and energy-efficient AIoT solutions for next-generation wireless sensing. © 1986-2012 IEEE.

关键词： Wireless local area networks (WLAN)