Deepfake Media Forensics: Status and Future Challenges
The rise of AI-generated synthetic media, or deepfakes, has introduced unprecedented opportunities and challenges across various fields, including entertainment, cybersecurity, and digital communication. Using advanced frameworks such as Generative Adversarial Networks (GANs) and Diffusion Models (D...
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MDPI AG
2025-02-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/11/3/73 |
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| author | Irene Amerini Mauro Barni Sebastiano Battiato Paolo Bestagini Giulia Boato Vittoria Bruni Roberto Caldelli Francesco De Natale Rocco De Nicola Luca Guarnera Sara Mandelli Taiba Majid Gian Luca Marcialis Marco Micheletto Andrea Montibeller Giulia Orrù Alessandro Ortis Pericle Perazzo Giovanni Puglisi Nischay Purnekar Davide Salvi Stefano Tubaro Massimo Villari Domenico Vitulano |
| author_facet | Irene Amerini Mauro Barni Sebastiano Battiato Paolo Bestagini Giulia Boato Vittoria Bruni Roberto Caldelli Francesco De Natale Rocco De Nicola Luca Guarnera Sara Mandelli Taiba Majid Gian Luca Marcialis Marco Micheletto Andrea Montibeller Giulia Orrù Alessandro Ortis Pericle Perazzo Giovanni Puglisi Nischay Purnekar Davide Salvi Stefano Tubaro Massimo Villari Domenico Vitulano |
| author_sort | Irene Amerini |
| collection | DOAJ |
| description | The rise of AI-generated synthetic media, or deepfakes, has introduced unprecedented opportunities and challenges across various fields, including entertainment, cybersecurity, and digital communication. Using advanced frameworks such as Generative Adversarial Networks (GANs) and Diffusion Models (DMs), deepfakes are capable of producing highly realistic yet fabricated content, while these advancements enable creative and innovative applications, they also pose severe ethical, social, and security risks due to their potential misuse. The proliferation of deepfakes has triggered phenomena like “Impostor Bias”, a growing skepticism toward the authenticity of multimedia content, further complicating trust in digital interactions. This paper is mainly based on the description of a research project called FF4ALL (FF4ALL-Detection of Deep Fake Media and Life-Long Media Authentication) for the detection and authentication of deepfakes, focusing on areas such as forensic attribution, passive and active authentication, and detection in real-world scenarios. By exploring both the strengths and limitations of current methodologies, we highlight critical research gaps and propose directions for future advancements to ensure media integrity and trustworthiness in an era increasingly dominated by synthetic media. |
| format | Article |
| id | doaj-art-2df7354457ec44d587ebf423130f2fd5 |
| institution | DOAJ |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-2df7354457ec44d587ebf423130f2fd52025-08-20T02:42:34ZengMDPI AGJournal of Imaging2313-433X2025-02-011137310.3390/jimaging11030073Deepfake Media Forensics: Status and Future ChallengesIrene Amerini0Mauro Barni1Sebastiano Battiato2Paolo Bestagini3Giulia Boato4Vittoria Bruni5Roberto Caldelli6Francesco De Natale7Rocco De Nicola8Luca Guarnera9Sara Mandelli10Taiba Majid11Gian Luca Marcialis12Marco Micheletto13Andrea Montibeller14Giulia Orrù15Alessandro Ortis16Pericle Perazzo17Giovanni Puglisi18Nischay Purnekar19Davide Salvi20Stefano Tubaro21Massimo Villari22Domenico Vitulano23Department of Computer, Control and Management Engineering, Sapienza University of Rome, 00185 Roma, ItalyDepartment of Information Engineering and Mathematics, University of Siena, 53100 Siena, ItalyDepartment of Mathematics and Computer Science, University of Catania, 95125 Catania, ItalyDepartment of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, 20133 Milano, ItalyDepartment of Information Engineering and Computer Science, University of Trento, 38123 Trento, ItalyDepartment of Basic and Applied Sciences for Engineering, Sapienza University of Rome, 00185 Roma, ItalyCNIT, National Inter-University Consortium for Telecommunications, 50134 Florence, ItalyDepartment of Information Engineering and Computer Science, University of Trento, 38123 Trento, ItalyIMT School for Advanced Studies, 55100 Lucca, ItalyDepartment of Mathematics and Computer Science, University of Catania, 95125 Catania, ItalyDepartment of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, 20133 Milano, ItalyDepartment of Computer, Control and Management Engineering, Sapienza University of Rome, 00185 Roma, ItalyDepartment of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, ItalyDepartment of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, ItalyDepartment of Information Engineering and Computer Science, University of Trento, 38123 Trento, ItalyDepartment of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, ItalyDepartment of Mathematics and Computer Science, University of Catania, 95125 Catania, ItalyDepartment of Information Engineering, University of Pisa, 56122 Pisa, ItalyDepartment of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, ItalyDepartment of Information Engineering and Mathematics, University of Siena, 53100 Siena, ItalyDepartment of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, 20133 Milano, ItalyDepartment of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, 20133 Milano, ItalyMIFT Department, University of Messina, Viale F. Stagno d’Alcontres, 31, 98166 Messina, ItalyDepartment of Basic and Applied Sciences for Engineering, Sapienza University of Rome, 00185 Roma, ItalyThe rise of AI-generated synthetic media, or deepfakes, has introduced unprecedented opportunities and challenges across various fields, including entertainment, cybersecurity, and digital communication. Using advanced frameworks such as Generative Adversarial Networks (GANs) and Diffusion Models (DMs), deepfakes are capable of producing highly realistic yet fabricated content, while these advancements enable creative and innovative applications, they also pose severe ethical, social, and security risks due to their potential misuse. The proliferation of deepfakes has triggered phenomena like “Impostor Bias”, a growing skepticism toward the authenticity of multimedia content, further complicating trust in digital interactions. This paper is mainly based on the description of a research project called FF4ALL (FF4ALL-Detection of Deep Fake Media and Life-Long Media Authentication) for the detection and authentication of deepfakes, focusing on areas such as forensic attribution, passive and active authentication, and detection in real-world scenarios. By exploring both the strengths and limitations of current methodologies, we highlight critical research gaps and propose directions for future advancements to ensure media integrity and trustworthiness in an era increasingly dominated by synthetic media.https://www.mdpi.com/2313-433X/11/3/73media forensicsdeepfake detectiondeepfake attribution and recognitiondeepfake authentication techniquesaudio deepfake detection |
| spellingShingle | Irene Amerini Mauro Barni Sebastiano Battiato Paolo Bestagini Giulia Boato Vittoria Bruni Roberto Caldelli Francesco De Natale Rocco De Nicola Luca Guarnera Sara Mandelli Taiba Majid Gian Luca Marcialis Marco Micheletto Andrea Montibeller Giulia Orrù Alessandro Ortis Pericle Perazzo Giovanni Puglisi Nischay Purnekar Davide Salvi Stefano Tubaro Massimo Villari Domenico Vitulano Deepfake Media Forensics: Status and Future Challenges Journal of Imaging media forensics deepfake detection deepfake attribution and recognition deepfake authentication techniques audio deepfake detection |
| title | Deepfake Media Forensics: Status and Future Challenges |
| title_full | Deepfake Media Forensics: Status and Future Challenges |
| title_fullStr | Deepfake Media Forensics: Status and Future Challenges |
| title_full_unstemmed | Deepfake Media Forensics: Status and Future Challenges |
| title_short | Deepfake Media Forensics: Status and Future Challenges |
| title_sort | deepfake media forensics status and future challenges |
| topic | media forensics deepfake detection deepfake attribution and recognition deepfake authentication techniques audio deepfake detection |
| url | https://www.mdpi.com/2313-433X/11/3/73 |
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