Download PDFOpen PDF in browserDeepfakes and Cybersecurity: Detection and MitigationEasyChair Preprint 1416212 pages•Date: July 25, 2024AbstractDeepfake technology, which leverages advanced artificial intelligence (AI) and machine learning techniques to create hyper-realistic but fabricated media, has emerged as a significant challenge to cybersecurity (Maras & Alexandrou, 2023). By manipulating audio and visual content to produce deceptive and convincing simulations, deepfakes have the potential to undermine trust in digital media and create a range of security risks (Chesney & Citron, 2021). This abstract provides an overview of the deepfake phenomenon, its implications for cybersecurity, and essential strategies for detection and mitigation. Deepfakes are generated using sophisticated algorithms, such as generative adversarial networks (GANs), which create highly realistic images, videos, and audio recordings of individuals (Goodfellow et al., 2014). These fabricated media can be used to deceive, manipulate, and defraud, presenting new threats to personal security, corporate integrity, and national security (Dewey, 2022). The proliferation of deepfake technology has led to growing concern about its potential misuse in various domains, including misinformation campaigns, identity theft, financial fraud, and political manipulation (Kietzmann et al., 2023). The cybersecurity implications of deepfakes are profound. They can be employed to impersonate individuals in phishing attacks, manipulate public opinion through false information, and disrupt organizational operations through misleading communications (Elish, 2023). For instance, deepfakes can facilitate social engineering attacks by creating convincing but fake video messages from trusted figures, tricking individuals into revealing sensitive information or performing unauthorized actions (Pope, 2022). Keyphrases: Cybersecurity, Deepfakes, Detection and Mitigation
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