Digital Image Tamper Detection Technique Based on Spectrum Analysis of CFA Artifacts

Sensors

卷:18

LuisJavier García Villalba¹  Edgar González Fernández¹  AnaLucila Sandoval Orozco¹  Julio Hernandez-Castro² 

[1] Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases 9, Ciudad Universitaria, 28040 Madrid, Spain;

[2] School of Computing, Office S129A, University of Kent, Cornwallis South Building, Canterbury CT2 7NF, UK;

关键词: Bayer Filter;    CFA artifacts;    Color Filter Array;    Discrete Cosine Transform;    Image Forensics;    image tamper detection;   

DOI  :  10.3390/s18092804

来源: DOAJ

【 摘 要 】

Existence of mobile devices with high performance cameras and powerful image processing applications eases the alteration of digital images for malicious purposes. This work presents a new approach to detect digital image tamper detection technique based on CFA artifacts arising from the differences in the distribution of acquired and interpolated pixels. The experimental evidence supports the capabilities of the proposed method for detecting a broad range of manipulations, e.g., copy-move, resizing, rotation, filtering and colorization. This technique exhibits tampered areas by computing the probability of each pixel of being interpolated and then applying the DCT on small blocks of the probability map. The value of the coefficient for the highest frequency on each block is used to decide whether the analyzed region has been tampered or not. The results shown here were obtained from tests made on a publicly available dataset of tampered images for forensic analysis. Affected zones are clearly highlighted if the method detects CFA inconsistencies. The analysis can be considered successful if the modified zone, or an important part of it, is accurately detected. By analizing a publicly available dataset with images modified with different methods we reach an 86% of accuracy, which provides a good result for a method that does not require previous training.

【 授权许可】

Unknown