A comparative analysis of image steganography based on DCT algorithm and steganography tool to hide nuclear reactors confidential information

Digital Steganography is the science that involves communicating secret data in an appropriate multimedia carrier, e.g., image, audio, and video files. Under this assumption, if the feature is visible, the point of attack is evident, thus the here goal is conceal to the existence of the embedded data .This is totally review paper, in which we provide the comparison of available steganography technology hiding text in an image file using Least Significant Bit (LSB) based Steganography, Discrete Cosine Transform (DCT) based Steganography and Discrete Wavelet Transform (DWT) based steganography. The LSB algorithm is implemented in spatial domain in which the payload bits are embedded into the least significant bits of cover image to derive the stego-image whereas DCT & DWT algorithm are implemented in frequency domain in which the stego -image is transformed from spatial domain to the frequency domain. Keywords—Steganography, Cryptography, Least Significant Bit (LSB); Discrete Cosine Transform (DCT); Discrete Wavelet Transform (DWT).

A Survey on various types of Steganography and Analysis of Hiding Techniques

This research proposes a method to enhance the payload message by embedding messages on the dilated edge areas by the Least Significant Bit (LSB) method. To add security aspects to messages, messages are not embedded directly on the LSB but encrypted with XOR operations with Most Significant Bit (MSB). The experimental results of the test in this study showed that the dilation process to some extent can increase the payload of 18.65% and the average bpp is 1.42 while maintaining the imperceptibilty quality of stego image with an average PSNR value of about 47 dB, SSIM is 0.9977 and MSE is 1.13.

/pdf/payload-enhancement-on-least-significant-bit-image-5ptqjm6qu9.pdf

Payload Enhancement on Least Significant Bit Image Steganography Using Edge Area Dilation

This paper presents a technique for transmitting information efficiently and securely, hiding confidential messages on seemingly innocent messages using steganography. The insertion technique in the least significant bit is used to insert images into digital pictures or other secret watermark. Artificial Neural Networks are used in the process of withdrawal of encrypted information acting as keys that determine the existence of hidden information.

Artificial Neural Networks Applied to Image Steganography

The fast growth in communication technologies and the increased availability of the public networks (Internet) facilitated data transfer. However the public communication channels are vulnerable to security attacks that may lead to unauthorized access to some information. Encryption has been used to persist and prevent these attacks. But when the information is decrypted it will be exposed to the attackers again and it will not have any security protection. Steganography is the science of embedding the secret messages inside other medium files (text, audio, image, and video) in a way that hides the existence of the secret message at all. Steganography applies an embedding process in which the redundant bits of the medium are replaced by the bits of the secret message. Image Steganography is the field of steganography in which the medium that used to carry the secret data is a digital image. Image Steganography is an important area of research in the recent years. This article reviews the steganography based on digital images; illustration of the concept and the common approaches are discussed. Also Steganalysis which is the science of attacking steganography will be briefly discussed. Keywords— digital image steganography; spatial domain embedding; transform domain embedding; cover-image; stego-image; wavelet transform; Steganalysis

/pdf/a-survey-on-digital-image-steganography-3arnzxtmo9.pdf

A Survey on Digital Image Steganography

Steganography is the technique of hiding the fact that communication is taking place, by hiding data in other data. Many different carrier file formats can be used, but digital images are the most popular because of their frequency on the Internet. For hiding secret information in images, there exist a large variety of steganographic techniques some are more complex than others and all of them have respective strong and weak points. Steganalysis, the detection of this hidden information, is an inherently difficult problem and requires a thorough investigation so we are using "Edge detection Filter". In this paper search how the edges of the images can be used to hiding text message in Steganography .It give the depth view of image steganography and Edge detection Filter techniques. Method: In this paper search how the edges of the images can be used to hiding text message in Steganography. For that gray image has been presented. In this method tried to find binary value of each character of text message and then in the next stage, tried to find dark places of gray image (black) by converting the original image to binary image for labeling each object of image by considering on 8 pixel connectivity. Then these images have been converted to RGB image in order to find dark places. Because in this way each sequence of gray color turns into RGB color and dark level of grey image is found by this way if the Gary image is very light the histogram must be changed manually to find just dark places. In the final stage each 8 pixels of dark places has been considered as a byte and binary value of each character has been put in low bit of each byte that was created manually by dark places pixels for increasing security of the main way of least Significant bit steganography. Steganalysis then used to evaluate the hiding process to ensure the data can be hidden in best possible way.

Image Steganography Using LSB and Edge - Detection Technique

: One of the most prevalent cancer forms, skin cancer has become more widespread in recent decades. It is critical to correctly diagnose skin lesions to distinguish between benign and malignant lesions in order to provide proper patient care. While there are various computerized approaches for classifying skin lesions, convolutional neural networks (CNNs) have been demonstrated to outperform traditional methods. We offer an hybrid CNN model for skin lesion categorization in this paper. We fuse an optimized pre-trained VGG-16 model with a pre-trained ResNet-50 CNN model to construct an hybrid model for skin lesion classification. In this work, we have used ISIC 2016-17 dermoscopic images to test the performance of the proposed hybrid CNN model. The proposed method achieves very good classification performance when tested on the ISIC Dataset, with an accuracy of 0.8565.

Skin Lesion Detection using VGG-16 and ResNet-50 based Hybrid CNN Model

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Image Steganography Using LSB and Edge - Detection Technique

Skin Lesion Detection using VGG-16 and ResNet-50 based Hybrid CNN Model