Image Steganography Using Edge Detection by Kirsch Operator and Flexible Replacement Technique
01 Jan 2019-pp 175-187
TL;DR: A novel data hiding technique in image using Kirsch operator, which has unique feature to find maximum edge strength in different orientations, which is sharable to intended receiver as a key and if appropriate reverse approach is taken, secret image can be successfully retrieved without any data loss.
Abstract: This paper proposes a novel data hiding technique in image using Kirsch operator, which has unique feature to find maximum edge strength in different orientations. Depending on a threshold value for the Kirsch operator and the intensity value of each pixel of cover image, a scale with 3 ranges would be created. This scale is the basis for choosing flexible, i.e., 2, 3, or 4 LSB replacements for steganographic encoding. The threshold value is sharable to intended receiver as a key and if appropriate reverse approach is taken, secret image can be successfully retrieved without any data loss. This work has been carried on different grayscale images, maximum payload has been calculated, PSNR and SSIM values are measured to get satisfactory result of quality. To ensure un-detectability, Bhattacharyya distance has been calculated and for robustness test, RS steganalysis attack has been performed.
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TL;DR: In this paper, a new edge detector called CLoG has been developed and is used for the detection of edge areas in digital images and secret information has been embedded in detected edges.
14 citations
TL;DR: In this paper, the authors highlight the pros and cons of the existing image steganography techniques and crypto-stego methods and provide a detailed description of commonly using evaluations parameters for both steganographers and cryptography.
Abstract: Recent advancement in the digital technology and internet has facilitated usage of multimedia objects for data communication. However, interchanging information through the internet raises several security concerns and needs to be addressed. Image steganography has gained huge attention from researchers for data security. Image steganography secures the data by imperceptibly embedding data bits into image pixels with a lesser probability of detection. Additionally, the encryption of data before embedding provides double-layer protection from the potential eavesdropper. Several steganography and cryptographic approaches have been developed so far to ensure data safety during transmission over a network. The purpose of this work is to succinctly review recent progress in the area of information security utilizing combination of cryptography and steganography (crypto-stego) methods for ensuring double layer security for covert communication. The paper highlights the pros and cons of the existing image steganography techniques and crypto-stego methods. Further, a detailed description of commonly using evaluations parameters for both steganography and cryptography, are given in this paper. Overall, this work is an attempt to create a better understanding of image steganography and its coupling with the encryption methods for developing state of art double layer security crypto-stego systems.
3 citations
TL;DR: Wang et al. as discussed by the authors used a three-channel convolutional neural network (T-CNN) composed of improved AlexNet, combined with YOLOv5, egg contour curve creation and support vector machine (SVM).
Abstract: ABSTRACT 1. In a non-cage environment, goose eggs are buried in litter and goose feathers, leading to contamination and discolouration. Such random distribution of goose eggs poses a great challenge to the recognition and location for intelligent picking by robot systems on farm. 2. In order to assist in recognition and location of goose eggs in non-cage environments, a novel method was proposed which used three-channel convolutional neural network (T-CNN), composed of improved AlexNet, combined with ‘you only look once’ (YOLOv5), egg contour curve creation and support vector machine (SVM). 3. Using this method, the original goose egg images were put into the YOLOv5 model for target detection and segmentation. In parallel, the median filter and maximum interclass variance method (OTSU) were applied to egg segmentation images to obtain the main pixels for each, and the Kirsch operator was used for edge extraction and contour curves fitting by designing the fitting curve equation to obtain segmentation images with goose egg contour curves. 4. In order to further enrich the differences between goose eggs and background, the goose egg segmentation images were divided into three colour components: R, G and B, which were put into T-CNN for feature extraction. Then the eggs were classified by vector stitching and SVM, by adding goose egg contour curve images. 5. The recognition and location results showed that about 95.65% of the goose egg pixel blocks in the segmented images were recognised correctly. About 3.81% of the pixel blocks in the segmented images were recognised incorrectly, and the centre of mass offset was about 4.45 pixels. 6. This study demonstrated accurate goose egg recognition and location using the proposed method in a non-cage environment. This highlighted its application prospect in intelligent goose egg picking as well as a possible method to use in other species laying eggs outside the nest (floor eggs) as for example laying hens in non cage systems.
01 Mar 2021
TL;DR: In this study, a new approach to masking information based on the incorporation of LSB replacement mechanism and edge detection is proposed, which achieves a much higher payload and better optical quality than that of the latest technology.
Abstract: Information security has become the main concern of the most famous researchers and the focus of their attention, as they are constantly trying to find the best and safest ways to transfer information through a secure tunnel to protect it from hacking attempts and common attacks on the Internet and in this research, we tried to embody one of the methods of security in protecting data. In this study, a new approach to masking information based on the incorporation of LSB replacement mechanism and edge detection is proposed. To avoid HVS mining when more covert bits are included in pixels, we classify cover pixels into edge regions and non-edge regions. Then, the pixels belonging to the edge region are used to transmit more secret bits. In addition, to increase the load as well as maintain good image quality, we adopt a clever method in which the edge information is identified by the most important bits (MSBs) of the cover image so that it does not need to be stored. At the extraction stage, the same edge information is obtained. Therefore, confidential data can be extracted correctly without confusion. Experimental results prove that our scheme achieves a much higher payload and better optical quality than that of the latest technology.
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03 Oct 1988TL;DR: This chapter discusses two Dimensional Systems and Mathematical Preliminaries and their applications in Image Analysis and Computer Vision, as well as image reconstruction from Projections and image enhancement.
Abstract: Introduction. 1. Two Dimensional Systems and Mathematical Preliminaries. 2. Image Perception. 3. Image Sampling and Quantization. 4. Image Transforms. 5. Image Representation by Stochastic Models. 6. Image Enhancement. 7. Image Filtering and Restoration. 8. Image Analysis and Computer Vision. 9. Image Reconstruction From Projections. 10. Image Data Compression.
8,504 citations
1,220 citations
TL;DR: A class of algorithms is described which enables computer quantized images to be decomposed into constituent reflecting the structure of the images, viewed as the morphological precursor to a higher level syntactic analysis.
590 citations
TL;DR: The experimental results show that the proposed scheme not only achieves high embedding capacity but also enhances the quality of the stego image from the HVS by an edge detection technique.
Abstract: Steganography is the art and science of hiding data into information. The secret message is hidden in such a way that no one can apart from the sender or the intended recipient. The least significant bit (LSB) substitution mechanism is the most common steganographic technique for embedding a secret message in an image with high capacity, while the human visual system (HVS) would be unable to notice the hidden message in the cover image. In this paper, besides employing the LSB substitution technique as a fundamental stage, we take advantage of edge detection technique. The experimental results show that the proposed scheme not only achieves high embedding capacity but also enhances the quality of the stego image from the HVS by an edge detection technique. Moreover, based on that the secret message is replaced with different LSBs, our scheme can effectively resist the image steganalysis.
221 citations
04 Jun 2007
TL;DR: The LSB embedding technique is explained, the evaluation results for 2,4,6 Least significant bits for a .pngfile and a .bmp file are presented and a strong focus on the LSB techniques in image Steganography is provided.
Abstract: Steganography is the art of hiding information in information is gaining momentum as it scores over cryptography because it enables to embedd the secret message to cover images. Steganographic techniques offer more promise in digital image processing. The Least Significant Bit embedding technique suggests that data can be hidden in the least significant bits of the cover image and the human eye would be unable to notice the hidden image in the cover file. This technique can be used for hiding images in 24-bit, 8-bit or gray scale format. We emphasize strongly on image Steganography providing a strong focus on the LSB techniques in image Steganography. This paper explains the LSB embedding technique and presents the evaluation results for 2,4,6 Least significant bits for a .png file and a .bmp file.
161 citations