AbhAS: A Novel Realistic Image Splicing Forensics Dataset

Abstract

This paper proposes a realistic image splicing dataset named AbhAS for evaluating various image forensic algorithms. We evaluate the performance of our proposed AbhAS dataset against existing benchmark datasets by extracting high-energy coefficients from images belonging to each dataset with the application of Kekre and discrete cosine transforms (DCT). Thus, we obtain feature sets of sizes 12, 24, and 48 respectively which are passed through various machine learning classifiers. RandomForest (with DCT) and Bagging (with Kekre transform) provide the highest detection accuracy. We believe this dataset could add value to the existing work in the area of image forensics.

Keywords:

• Image splicing
• image forensics
• realistic datasets
• machine learning
• Kekre transforms
• WEKA tool

Acknowledgments

Angelina L. Gokhale acknowledges Symbiosis International (Deemed University) for granting the junior research fellowship. The authors also acknowledge the WEKA tool creators and authors of benchmark image splicing datasets. The authors wish to thank Mandaar Pande for his valuable comments.

Disclosure statement

No potential conflict of interest was reported by the author(s).