Date of Publication :29th March 2024

Abstract:DeepFake technology has transformed the production and manipulation of visual media and raised concerns regarding the veracity and authenticity of digital content. DeepFake technology is driven by artificial intelligence algorithms. The widespread use of DeepFakes poses serious problems for a number of industries, including cybersecurity, politics, entertainment, and journalism. The identification of DeepFakes has become an important area of study, requiring the creation of reliable and efficient detection tools to counteract their negative effects. A thorough assessment of DeepFake detection techniques, ranging from sophisticated machine learning algorithms to conventional forensic analysis, is presented in this work. We examine the fundamentals of DeepFake creation and the strategies used by malicious actors to create believable synthetic content. Additionally, we evaluate current DeepFake detection systems, examining their limitations, capabilities, and potential uses. By using case studies and empirical evaluations, we analyze the effectiveness of existing detection methods and pinpoint significant roadblocks to reducing the threat posed byDeepFakes. We also discuss the ethical implications and societal repercussions of DeepFaketechnology, highlighting the need of esponsible innovation and teamwork in addressing emerging hazards. By deepening our knowledge of DeepFake detection and facilitating multidisciplinary cooperation, we may better prepare people and institutions to navigate the complex terrain of digital authenticity with assurance and adaptability.

Reference :