In this era of advanced generative artificial intelligence (AI), in which machine-generated content coexists with human-created content, the question of authenticity extends beyond the binary “real or fake.” Media forensics must evolve to encompass three crucial dimensions. Provenance: was the content created by a person, AI, or a combination of both? Intention: was the content created with a specific purpose, such as to inform, entertain, deceive, or manipulate? Context: how is the content being presented, used, and interpreted within its particular context? As AI-generated content becomes increasingly prevalent, the focus should shift towards ensuring transparency, ethical use, and accountability in content creation and dissemination, regardless of its origin.
In this paper, we present a comprehensive countermeasure framework designed to address a broad spectrum of attacks related to generative AI, where merely distinguishing between “real” and “fake” content is no longer adequate. To highlight the necessity of this new perspective, we present two case studies that demonstrate the feasibility and effectiveness of our proposed framework. By implementing such a framework, we can more effectively navigate the challenges posed by advanced generative AI while harnessing the opportunities it presents.
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APSIPA Transactions on Signal and Information Processing Special Issue - Deepfakes, Unrestricted Adversaries, and Synthetic Realities in the Generative AI Era
See the other articles that are part of this special issue.