A novel blind tamper detection and localization scheme for multiple faces in digital images

Abstract Face image manipulation detection (FIMD) is a research area of great interest, widely applicable in fields requiring data security and authentication. Existing FIMD schemes aim to identify manipulations digital face images, but they possess individual strengths limitations. Most can only detect specific under certain conditions, leading variable success rates across different images. The literature lacks emphasis on detecting involving multiple faces. This paper introduces novel blind tamper localization scheme specifically designed for faces proposed (MFMD) consists two stages: selection, watermarking. Through extensive experiments, the MFMD scheme's performance has been evaluated various multiple‐face considering embedding capacity, payload, watermarked quality, time complexity, ability. results demonstrate efficacy types Furthermore, images exhibit high visual even when are present. efficiency recommends it practical applications, especially sharing personal over unsecured networks. advances techniques by addressing neglected detection. With improved accuracy, faster processing times, resilience against manipulations, offers valuable capabilities enhancing authentication real‐world scenarios.