AbhAS: A Novel Realistic Image Splicing Forensics Dataset

• Published in: Journal of applied security research Vol. 17; no. 1; pp. 80 - 102
• Main Authors: Gokhale, Angelina L., Thepade, Sudeep D., Aarons, Nikhil R., Pramod, Dhanya, Kulkarni, Ravi
• Format: Journal Article
• Language: English
• Published: Routledge 02-01-2022
• Subjects: image forensics; Image splicing; Kekre transforms; machine learning; realistic datasets; WEKA tool
• ISSN: 1936-1610; 1936-1629
• DOI: 10.1080/19361610.2020.1811059

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.