Seam Carving Research Papers - Academia.edu (original) (raw)
This paper presents a new approach called S eam Carving for multirate signal processing of digital images. Increasing and decreasing the sizes of digital images are common place in day-today image processing. These methods involve long... more
This paper presents a new approach called S eam Carving for multirate signal processing of digital images. Increasing and decreasing the sizes of digital images are common place in day-today image processing. These methods involve long procedures and sometimes consume more time for getting implemented. Whereas, using the S eam Carving method eradicates the excess time involved in upsampling and downsampling a digital image considerably as this method is straightforward and simple to implement. This method comes in handy when we are dealing with large medical images and remotely sensed images. This technique is applied on standard images and its performance is analyzed. The entire work was implemented using Matlab R2017a software package.
Nowadays there are many different devices for displaying images. According to using images in different applications and various devices, displaying the images in different display size is vital and essential. Therefore it is very... more
Nowadays there are many different devices for displaying images. According to using images in different applications and various devices, displaying the images in different display size is vital and essential. Therefore it is very important to preserve content of images during resizing. In this paper, we present two different methods for content aware image resizing that combined with Seam carving algorithm and scaling. In both methods, at first the important regions of image are determined. After that Seam carving algorithm is used in these important regions and traditional scaling is applied for other regions. The proposed approaches are implemented and the experimental result shows that these methods are appropriate for various images especially in content images with many objects and edges.
Steganalysis and forgery detection in image forensics are generally investigated separately. We have designed a method targeting the detection of both steganography and seam-carved forgery in JPEG images. We analyze the neighboring... more
Steganalysis and forgery detection in image forensics are generally investigated separately. We have designed
a method targeting the detection of both steganography and seam-carved forgery in JPEG images.
We analyze the neighboring joint density of the DCT coefficients and reveal the difference between the untouched
image and the modified version. In realistic detection, the untouched image and themodified version
may not be obtained at the same time, and different JPEG images may have different neighboring joint density
features. By exploring the self-calibration under different shift recompressions, we propose calibrated
neighboring joint density-based approaches with a simple feature set to distinguish steganograms and tampered
images from untouched ones. Our study shows that this approach has multiple promising applications
in image forensics. Compared to the state-of-the-art steganalysis detectors, our approach delivers better or
comparable detection performances with a much smaller feature set while detecting several JPEG-based
steganographic systems including DCT-embedding-based adaptive steganography and Yet Another Steganographic
Scheme (YASS). Our approach is also effective in detecting seam-carved forgery in JPEG images. By
integrating calibrated neighboring density with spatial domain rich models that were originally designed for
steganalysis, the hybrid approach obtains the best detection accuracy to discriminate seam-carved forgery
from an untouched image. Our study also offers a promising manner to explore steganalysis and forgery
detection together.
Retargeting images by seam carving and seam insertion is hard to identify; therefore, detection of seam tampered images has been an important but challenging research topic. Aside from existing methods, i.e., those derived from... more
Retargeting images by seam carving and seam insertion is hard to identify; therefore, detection of seam tampered images has been an important but challenging research topic. Aside from existing methods, i.e., those derived from steganography attacks and those based on statistical features, we have proposed a novel detection method in our previous work, referred to as the patch analysis method. This method divides images into 2 × 2 blocks, named as mini-squares, and then searches for one of nine patch types that is likely to recover a mini-square from seam carving. By analyzing the patch transition probability among three-connected mini-squares, we achieved currently best accuracies for detecting seam carved images. Here we extend the application of this method to detect imagestampered with seam insertion. We will present and discuss the experimental results in this paper.