Illuminating the Shadows - Challenges and Risks of Generative AI in Computer Vision for Brands
Keywords: Generative AI, computer vision, LLM, prompt engineering, text-to-image, image-to-text, brand detection, deepfakes.
TL;DR: Risks of Generative AI in Computer Vision for Brands and possible mitigations using modern LLMs and legal policies.
Abstract: The rapid advancements in generative AI have significantly transformed computer vision, presenting both opportunities and challenges for brands. This paper digs into the risks associated with the use of generative AI in computer vision applications, particularly focusing on brand integrity, detection and security. One primary concern is the ethical implications, where LLMs can amplify biases, produce fake product images and propagate harmful stereotypes, affecting brand reputation. The rise of deepfakes and AI-generated content poses a substantial risk of disinformation, leading to potential misuse in creating misleading advertisements or damaging a brand's image through falsified media.
Legal challenges are another critical aspect, especially concerning intellectual property rights and copyright issues. The ability of generative AI to produce content indistinguishable from original works raises questions about owner-ship, detection techniques and the legal frameworks required to protect brands. To address these tasks, the paper explores various generation, detection, and mitigation strategies emphasizing the importance of developing responsible and trustworthy generative AI technologies. By highlighting these issues, the paper aims to foster a balanced discourse on the ethical and practical aspects of generative AI in computer vision for brands, shares detection results, and suggests mitigation strategies.
Submission Number: 1
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