An Adaptive Reversible Data Hiding Scheme for JPEG Images
17 Sep 2016-pp 456-469
TL;DR: Experimental results demonstrate that the proposed adaptive reversible data hiding method can achieve a higher image quality and a less increased file size compared to the current state-of-the-art RDH method for JPEG images.
Abstract: Currently JPEG is the most popular image file format and the majority of images are stored in JPEG format due to storage constraint. Recently, reversible data hiding (RDH) for JPEG images draws researchers attention and has been developed rapidly. Due to the compression, performing RDH on a typical JPEG image is much more difficult than that on an uncompressed image. In this paper, we propose an adaptive reversible data hiding method for JPEG images, which is based on histogram shifting. We propose to select the optimal expandable bins-pair at image level by adopting a k-th nearest neighbors (KNN) algorithm. By developing a new block selection strategy, we can adaptively select the to-be-embedded blocks. Then, the message bits are embedded into the selected blocks at a specific bins-pair via the histogram shifting algorithm. Experimental results demonstrate that our proposed method can achieve a higher image quality and a less increased file size compared to the current state-of-the-art RDH method for JPEG images.
Citations
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TL;DR: Two simple yet effective detection methods for fake colorized images are proposed: Histogram-basedfake colorized image detection and feature encoding-based fake colorize image detection, which exhibit a decent performance against multiple state-of-the-art colorization approaches.
Abstract: Image forensics aims to detect the manipulation of digital images. Currently, splicing detection, copy-move detection and image retouching detection are drawing much attentions from researchers. However, image editing techniques develop with time goes by. One emerging image editing technique is colorization, which can colorize grayscale images with realistic colors. Unfortunately, this technique may also be intentionally applied to certain images to confound object recognition algorithms. To the best of our knowledge, no forensic technique has yet been invented to identify whether an image is colorized. We observed that, compared to natural images, colorized images, which are generated by three state-of-the-art methods, possess statistical differences for the hue and saturation channels. Besides, we also observe statistical inconsistencies in the dark and bright channels, because the colorization process will inevitably affect the dark and bright channel values. Based on our observations, i.e., potential traces in the hue, saturation, dark and bright channels, we propose two simple yet effective detection methods for fake colorized images: Histogram based Fake Colorized Image Detection (FCID-HIST) and Feature Encoding based Fake Colorized Image Detection (FCID-FE). Experimental results demonstrate that both proposed methods exhibit a decent performance against multiple state-of-the-art colorization approaches.
56 citations
Cites background from "An Adaptive Reversible Data Hiding ..."
...The active techniques usually refer to watermarking techniques [6-8], which embed authentication information to the to-beprotected images....
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TL;DR: Zhang et al. as discussed by the authors proposed two simple yet effective detection methods for fake colorized images: Histogram-based fake colored image detection and feature encoding-based false colorized image detection.
Abstract: Image forensics aims to detect the manipulation of digital images. Currently, splicing detection, copy-move detection, and image retouching detection are attracting significant attention from researchers. However, image editing techniques develop over time. An emerging image editing technique is colorization, in which grayscale images are colorized with realistic colors. Unfortunately, this technique may also be intentionally applied to certain images to confound object recognition algorithms. To the best of our knowledge, no forensic technique has yet been invented to identify whether an image is colorized. We observed that, compared with natural images, colorized images, which are generated by three state-of-the-art methods, possess statistical differences for the hue and saturation channels. Besides, we also observe statistical inconsistencies in the dark and bright channels, because the colorization process will inevitably affect the dark and bright channel values. Based on our observations, i.e., potential traces in the hue, saturation, dark, and bright channels, we propose two simple yet effective detection methods for fake colorized images: Histogram-based fake colorized image detection and feature encoding-based fake colorized image detection. Experimental results demonstrate that both proposed methods exhibit a decent performance against multiple state-of-the-art colorization approaches.
40 citations
08 Jun 2018
TL;DR: Compared to some state-of-the-art RDH methods for JPEG images, experimental results show the superiority of the proposed methods both in image quality and increased file size.
Abstract: The joint photographic experts group (JPEG) is the most popular image format in our daily life, and it is widely used by digital cameras and other photographic capture devices. Recently, reversible data hiding (RDH) for JPEG images has become an active research area in the field of data hiding. In this paper, a new two-dimensional coefficient-histogram based RDH scheme for JPEG image is proposed. First, a two-dimensional quantized discrete cosine transform (DCT) coefficient-histogram is generated. Then, data are embedded according to a specifically designed coefficient-pair-mapping (CPM). Here, by the proposed approach, compared with the one-dimensional histogram-based RDH for JPEG images, the increased file size is minimized. Moreover, the selection strategy based on the optimal frequency band of the DCT coefficient-pairs is proposed, by which the distortion of the marked JPEG image is minimized. Compared to some state-of-the-art RDH methods for JPEG images, experimental results show the superiority of our methods both in image quality and increased file size.
3 citations
TL;DR: In this paper, an Advanced Fake Image-Feature Network (AFIFN) based on deep learning methods was proposed for image forgery detection, in which Discrete Cosine Transformation (DCT) and Y Cr Cb based image pre-processing is employed.
1 citations
07 Sep 2020
TL;DR: Two simple yet effective detection method Histogram based and Feature Encoding based Fake Colorized Image Detection along with the dense convolution network are proposed for detecting the fake colorized images.
Abstract: Image forgery implies altering the digital image to some meaningful or valuable data. Image forensics is a well developed field that analyzes the images of specific conditions to build up trust and genuineness. Although image editing techniques can provide significant appreciation of the image or entertainment value, they may also be used with malicious intent. An emerging image editing technique is colorization, in which gray scale images are colorized with realistic colors. But this technique may also be intentionally applied to certain images to confound object recognition algorithms. In this work, it is observed that, colorized images, usually change the images using a variety of mechanisms. The digital image developed from the colorization Method possess statistical differences in their RGB channels, hue and saturation channels and also need to observe statistical inconsistencies in the dark and bright channels, because the colorization process will mainly affect the dark and bright channel values. Based on the experiments in the hue, saturation, dark and bright channels, two simple yet effective detection method Histogram based and Feature Encoding based Fake Colorized Image Detection along with the dense convolution network are proposed for detecting the fake colorized images
References
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TL;DR: Kernel and nearest-neighbor regression estimators are local versions of univariate location estimators, and so they can readily be introduced to beginning students and consulting clients who are familiar with such summaries as the sample mean and median.
Abstract: Nonparametric regression is a set of techniques for estimating a regression curve without making strong assumptions about the shape of the true regression function. These techniques are therefore useful for building and checking parametric models, as well as for data description. Kernel and nearest-neighbor regression estimators are local versions of univariate location estimators, and so they can readily be introduced to beginning students and consulting clients who are familiar with such summaries as the sample mean and median.
4,298 citations
"An Adaptive Reversible Data Hiding ..." refers methods in this paper
...ABBS proposes to find the different bins-pairs for different images by adopting a KNN (k-nearest neighbors) algorithm [1]....
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TL;DR: The author provides an overview of the JPEG standard, and focuses in detail on the Baseline method, which has been by far the most widely implemented JPEG method to date, and is sufficient in its own right for a large number of applications.
Abstract: A joint ISO/CCITT committee known as JPEG (Joint Photographic Experts Group) has been working to establish the first international compression standard for continuous-tone still images, both grayscale and color. JPEG's proposed standard aims to be generic, to support a wide variety of applications for continuous-tone images. To meet the differing needs of many applications, the JPEG standard includes two basic compression methods, each with various modes of operation. A DCT (discrete cosine transform)-based method is specified for 'lossy' compression, and a predictive method for 'lossless' compression. JPEG features a simple lossy technique known as the Baseline method, a subset of the other DCT-based modes of operation. The Baseline method has been by far the most widely implemented JPEG method to date, and is sufficient in its own right for a large number of applications. The author provides an overview of the JPEG standard, and focuses in detail on the Baseline method. >
3,425 citations
"An Adaptive Reversible Data Hiding ..." refers methods in this paper
...However, due to the characteristics of compression, most of the ordinary RDH methods cannot be directly applied to compressed images such as JPEG [16] images....
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TL;DR: The redundancy in digital images is explored to achieve very high embedding capacity, and keep the distortion low, in a novel reversible data-embedding method for digital images.
Abstract: Reversible data embedding has drawn lots of interest recently Being reversible, the original digital content can be completely restored We present a novel reversible data-embedding method for digital images We explore the redundancy in digital images to achieve very high embedding capacity, and keep the distortion low
2,739 citations
"An Adaptive Reversible Data Hiding ..." refers methods in this paper
...Ordinary RDH algorithms, such as histogram shifting [9], difference expansion [6,14], pixel groups’ geometric structure [18], DNA XNOR [15] etc....
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TL;DR: It is proved analytically and shown experimentally that the peak signal-to-noise ratio of the marked image generated by this method versus the original image is guaranteed to be above 48 dB, which is much higher than that of all reversible data hiding techniques reported in the literature.
Abstract: A novel reversible data hiding algorithm, which can recover the original image without any distortion from the marked image after the hidden data have been extracted, is presented in this paper. This algorithm utilizes the zero or the minimum points of the histogram of an image and slightly modifies the pixel grayscale values to embed data into the image. It can embed more data than many of the existing reversible data hiding algorithms. It is proved analytically and shown experimentally that the peak signal-to-noise ratio (PSNR) of the marked image generated by this method versus the original image is guaranteed to be above 48 dB. This lower bound of PSNR is much higher than that of all reversible data hiding techniques reported in the literature. The computational complexity of our proposed technique is low and the execution time is short. The algorithm has been successfully applied to a wide range of images, including commonly used images, medical images, texture images, aerial images and all of the 1096 images in CorelDraw database. Experimental results and performance comparison with other reversible data hiding schemes are presented to demonstrate the validity of the proposed algorithm.
2,240 citations
18 May 2011
TL;DR: This paper summarizes the first international challenge on steganalysis called BOSS (an acronym for Break The authors' Steganographic System), explaining the motivations behind the organization of the contest, its rules together with reasons for them, and the steganographic algorithm developed for the contest.
Abstract: This paper summarizes the first international challenge on steganalysis called BOSS (an acronym for Break Our Steganographic System). We explain the motivations behind the organization of the contest, its rules together with reasons for them, and the steganographic algorithm developed for the contest. Since the image databases created for the contest significantly influenced the development of the contest, they are described in a great detail. Paper also presents detailed analysis of results submitted to the challenge. One of the main difficulty the participants had to deal with was the discrepancy between training and testing source of images - the so-called cover-source mismatch, which forced the participants to design steganalyzers robust w.r.t. a specific source of images. We also point to other practical issues related to designing steganographic systems and give several suggestions for future contests in steganalysis.
902 citations