Investigating Authenticity-Related Features of Prominent AI Logo-Generators

Abstract

AI logo-generators have rapidly transformed the landscape of logo design by offering fast, cost-effective, and technically sophisticated tools for creating visual identities. However, persistent concerns about the authenticity and originality of AI-generated designs have raised important questions about creative ownership, ethical accountability, and the role of human input in automated design processes. This study investigates the authenticity-related features embedded in 20 widely used AI logo-generators to determine how algorithmic and interactive functions influence the production of distinctive and brand-authentic outcomes. Five key variables were systematically evaluated; algorithmic sophistication, template diversity, customisation depth, collaboration and feedback functions, and licensing and ownership rights, through a structured scoring matrix designed for cross-platform comparison. Our study suggests that platforms offering advanced prompt control, diverse templates, and flexible customisation options are more likely to produce designs with higher authenticity potential, whereas systems that prioritise automation often generate more generic visual patterns. Future research should generate samples using the selected platform to validate this finding. To strengthen authenticity assurance, a pilot study integrating the Exclusion Zone Mapping (EZM)—which identifies visual saturation and innovation zones to guide originality—and Provenance Metadata (PM) frameworks was applied to 50 selected AI-generated logos in the housing design sector. The finding demonstrated that the approach effectively eliminated repetitive motifs, validating EZM as a creative boundary system that promotes authenticity and, when combined with PM, aligns ethical verification with AI-driven design integrity. Together, EZM and PM establish a transparent and verifiable approach that transforms authenticity from an aesthetic ideal into a measurable and evidence-based practice. Although the sample of 20 platforms cannot represent all available tools, research provides actionable insights for designers, educators, and developers seeking to enhance authenticity, integrity, and creative accountability within AI-assisted design ecosystems.