Datasets, Clues and State-of-the-Arts for Multimedia Forensics: An Extensive Review

TL;DR

This comprehensive review analyzes multimedia tampering detection methods using deep learning, covering datasets, clues, architectures, state-of-the-art techniques, and future research directions in the field.

Contribution

It provides an extensive overview of benchmark datasets, tampering clues, deep learning architectures, and categorizes current detection methods with critical insights and future research gaps.

Findings

Deepfake detection methods show promising results.

Benchmark datasets enable standardized evaluation.

Traditional methods are being surpassed by deep learning approaches.

Abstract

With the large chunks of social media data being created daily and the parallel rise of realistic multimedia tampering methods, detecting and localising tampering in images and videos has become essential. This survey focusses on approaches for tampering detection in multimedia data using deep learning models. Specifically, it presents a detailed analysis of benchmark datasets for malicious manipulation detection that are publicly available. It also offers a comprehensive list of tampering clues and commonly used deep learning architectures. Next, it discusses the current state-of-the-art tampering detection methods, categorizing them into meaningful types such as deepfake detection methods, splice tampering detection methods, copy-move tampering detection methods, etc. and discussing their strengths and weaknesses. Top results achieved on benchmark datasets, comparison of deep learning approaches against traditional methods and critical insights from the recent tampering detection methods are also discussed. Lastly, the research gaps, future direction and conclusion are discussed to provide an in-depth understanding of the tampering detection research arena.