Performance analysis of data hiding in thermal image using alpha blending technique

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Abstract

Hiding data in an image become a very successful technique to communicate with end users, blinding the interpreter’s intention. Various algorithms have been analyzed on the basis of masking the content within the coordinates of the images or in the color of the images used. Few advanced developed complex algorithms to hide the content in the values of the colors to ensure or guarantee robustness of the data hidden. In this research, a simple technique called alpha blending is used to hide the data in an image and its impact is analyzed for different alpha values. The unavoidable impact of noise on the transmitted carrier image was formulated and studied by many researchers using various available algorithms. In this regard: the same is analyzed using alpha blending in thermal images.

Introduction

Steganography is the process of secret communication; it is the art of dissimulating information into digital mediums such as image [1]. Recent development in the field of secret communication has generated numerous benefits in the cyber world, which landed up in low -cost networking facilities. On the other hand, this easy access has paved route cause for security threat. Hence, authenticating every data communicated between users has become unavoidable. This necessity ceases illegal distribution of any content without proper permission. Anirban patra et al., proposed watermarking with different values of alpha blending, resulting all the images containing the watermarked information [5], [2]. Watermarking is the best technique used more frequently to authorize the product, and helps to trace the origin of the product. In this research, an invisible watermarking technique is used to entertain steganographic concept by hiding the existence of the data in the carrier file. Anirban Patra et al., described that, Alpha blending is used to display an alpha bitmap and an alpha bitmap is that one which has transparent and semi-transparent pixels, of the color images and grey color images. In this research, the alpha blending is analyzed in thermal images. Andrew Glasser stated that Alpha blending originated from computer graphics to composite enormous number of pixels. Bo Shen et al., illustrated that the use of Discrete Cosine Transform domain convolution theorems are used in a video file or image file only when they are in JPEG or MPEG bitstream in a typical video editing application such as video bluescreen editing [3].

Blinn stated that the Alpha value is 0, then the new pixel is transparent and does not affect the frame buffer, while if it’s 1, the new pixel is opaque and completely replaces the current frame buffer color [11]. Hence, in this research, it has been proposed to analyze the characteristics of the alpha blending value for different alpha value upon thermal images thereby producing a new blended color. The degree of the foreground color’s translucency may range from completely transparent and the blended color will be the background color.

Sravanthi et al., discussed the attacks on the spatial domain and Transform domain steganographic algorithms and the observations were based on noise minimization in the cover image [4]. In this proposed method, the fusion technique aimed to be the noise that collapse the original quality of the image at the interpreters view point. Only to the desired user the noise will be retreated to an information.

Section snippets

Methodology

Alpha blending is the process of combining an image with a background to create the appearance of partial or full transparency. However, semi transparency never reaches the potential as in full transparency [9]. In addition to the RGB component in each color, the fourth component exists in as colors called “alpha”, that refers to the transparency when it is drawn on top of another image. Any channel that defines the color values for pixels in an image, to identify the transparency in the

Red mask

Pixels are the smallest element of the picture, that is reserved for transparency information [8]. Upon masking the red color, the experimental analysis shows that the alpha blending produces best results for 0.1 as 60.51 mean squared error, 1.03 peak SNR and 0.03 as the SNR value for the watermarked image. The below mentioned Table 1 shows that the alpha blending concept has given efficient output.

Green mask

The same way like Red and Blue color Green mask is also giving better performance in MSE, PSNR and SNR for the value 0.1 is shown in Table 3.

The coordinates of the red components are fused with the thermal image by averaging concepts. The pixel values of the carrier image are directly summed up with the thermal image values. For example, the first component (1,1) of the carrier image and the (1,1) pixel of the information image is fused using the below formulae.Fx,y=SummationofCx,y+Ix,y

This

Results and discussions

In order to prevent the relevant features from disappearing, the alpha blending assigned with different size, instead of using the similar or same sized images for the cover as well the information image. The results obtained in the research of performing alpha blending concept for different sized carrier image and different sized stego image, it is noted that the PSNR goes low, which proves that the quality of the output image is less compared to the general standard ranges. When the

CRediT authorship contribution statement

S.Rathika: Conceptualization, Formal Analysis, Investigation, Methodology, Writing - review & editing. R.Gayathri: Validation, Visualization, Writing - original draft.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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    However, the qualitative assessment of the dataset showed that the variations were not significant to disrupt the data analysis. The fusion was performed at the pixel level, where the information was merged using the alpha-blending method [59]. Fig. 4 shows individual thermal and visible images and the fusion results, which merged visual and IRT modes.

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