With the rapid development of image processing technology, it is increasingly easy to falsify digital images without leaving an obvious visual trace. Today, seeing no longer allows you to believe [1]. Image counterfeiting, like any other illegal and pernicious activity, could cause serious harm to society. Digital image forensics is a new and emerging field. The goal of image analysis is to detect whether an image has been tampered with. With the widespread use of high-resolution digital cameras and highly advanced photo editing software, image tampering has become more common and it may often be necessary to resample (resize/rotate/stretch) the image to give it a natural appearance. Therefore, verifying the authenticity of digital images has become a very important issue. JPEG is one of the most commonly used compression systems in many practical applications. Therefore, JPEG image analysis has recently attracted increasing attention. Typically, two important properties are available for forensic analysis. The first and most obvious property is blocking artifacts in the spatial domain. Due to block-based processing in lossy JPEG compression, discontinuous pixels usually appear in the boundary between two adjacent 8 × 8 blocks. Such a blocking signature can serve as proof of JPEG compression [2] and some tampering operations [3]. Another important property concerns quantization artifacts in the DCT frequency domain. During JPEG compression, each DCT frequency component of the 8 × 8 block is quantized by a quantization step. This will lead to a specific shape of the corresponding DCT histogram. In other words, these dequantized coefficients will simply be grouped into the multiples of the quantization step. Combined with Laplac...... middle of article ...... the disparity map was based on belief propagation and mean shift segmentation [19]. The disparity map and the reference image (JI_L) are segmented into certain objects. The objects and the average disparity of these objects are noted respectively O_(JI_L)^i & d_(JI_L)^i, i = 1,2,…,m. If d_(JI_L)^i is in [D_b,D_f ], O_(JI_L)^i is considered the main content, O_(JI_L)^i∈O_maipart. If d_(JI_L)^ii is not in [D_b,D_f ], O_(JI_L)^i is considered as the background, O_(JI_L)^i∈O_background. In other words, O_(JI_L)^i∈{█(O_mainpart d_(JI_L)^i∈[D_b,D_f ] @O_background d_(JI_L)^i∉[D_b,D_f ] )→(1)┤A seam is a optimal path of eight connected pixels on a single JPEG image from top to bottom (vertical) and consisting of one and only one pixel in each row, which ensures that the JPEG image maintains a rectangle when the seams are removed. In [20], an energy function defines the