Seam carving, which preserves semantically important image content during resizing process, has been actively researched in recent years. This paper proposes a novel forensic technique to detect the trace of seam carving. We exploit the energy bias and noise level of images under analysis to reliably unveil the evidence of seam carving. Furthermore, we design a detector investigating the relationship among neighboring pixels to estimate the inserted seams. Experimental results from a large set of test images indicates the superior performance of the proposed methods for both seam carving and seam insertion.

In this paper, we propose an improved seam merging method for content-aware image resizing. This method merges a two-pixel-width seam element into one new pixel in image reduction and inserts a new pixel between the two pixels in image enlargement. To preserve important contents and structure, our method uses energy terms associated with importance and structure. Our method preserve the main structures by using a cartoon version of the original image when calculating the structure energy. In addition, we introduce a new energy term to suppress the distortion generated by excessive reduction or enlargement in iterated merger or insertion. Experimental results demonstrate that the proposed method can produce satisfactory results in both image reduction and enlargement.