In image forging

geometric transformations

JPEG compression

and blurring are typical operations. In this manuscript

algorithms for detection of the typical operations in a forged image are proposed based on operational characteristics. First

a possibly composite image is divided into overlapping blocks

and a block measure factor is defined and adopted to describe both re-sampling and JPEG compression characteristics for each block

followed by detection of tampered regions. Experimental results show that compared with the existing single targeted detection methods

the proposed algorithm can recognize forged images under more combinations of tampering modes and the tampered regions are located more effectively. Furthermore

the proposed method performs well even when the JPEG quality factor is small. Second

an approach is proposed to detect blurring traces. The image is blurred again with an appropriate blurring kernel and the difference of image pixels are estimated before and after double blurring. The tampered regions may be detected through the classification of the values in the difference image. Simulation results show that the algorithm is effective for blurring detection with various blurring operations and it also robust against lossy JPEG compression. Comparing with the existing block-based methods

the proposed method can reduce the computational complexity greatly by avoiding the point-by-point block calculation

and can detect small fuzzy traces.