Detecting tampering in audio-visual content using QIM watermarking

Abstract

This paper presents a framework for detecting tampered information in digital audio-visual content. The proposed framework uses a combination of temporal and spatial watermarks that do not decrease the quality of host videos. A modified version of the Quantization Index Modulation (QIM) algorithm is used to embed watermarks. The fragility of the QIM watermarking algorithm makes it possible to detect local, global, and temporal tampering attacks with pixel granularity. The technique is also able to identify the type of tampering attack. The framework is fast, robust, and accurate.