Fig. 1. Overview of the proposed method

Fig. 2. Texture distortion blocks of DIBR image and corresponding Benford’s law feature values. (a) Reference block and black hole block; (b) geometric distortion block; (c) blurry block; (d) incorrect texture block; (e) corresponding feature value

Fig. 3. Traditional images with different distortion levels and corresponding Benford’s law feature values. (a) First-level Gaussian blur; (b) second-level Gaussian blur; (c) third-level Gaussian blur; (d) fourth-level Gaussian blur; (e) feature values extracted from six traditional images with different distortion levels

Fig. 4. The first digit probability of DCT coefficients of images with different quality

Fig. 5. DCT coefficients divided along the three directions of 45°, 90°, and 135°

Fig. 6. MSCN coefficients with different distortion

Fig. 7. Result of different training ratios

Fig. 8. Results of different distortion types and different sequences. (a) Results of six conventional distorted images; (b) results of nine sequences of images

Table 1. Information of databases used in the experiments

Table 2. Comparison of different algorithms on three datasets

Table 3. Separate experiment of three features