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Top1. Introduction
Due to the development in technology, the transmission of data from one place to another is easy and fast. Whereas, the breach of information can be done through the advance tools and techniques. Sometimes, the leaked information may cause the severe losses. Information hiding techniques are used to resolve this problem (Kumar and Kumar, 2010). These techniques are able to conceal the important data in a way so that the intruder is unable to reveal the secret data. These are widely used in business and army for secret data communication. These techniques are broadly categorized into three classes namely, watermarking, cryptography, and steganography (Girdhar and Kumar, 2018). In watermarking, the watermark is added to the data for their authenticity. The watermark can be text, image, and audio. Watermark can be visible or invisible according to the applicability of watermarking in a specific area. However, this technique reveals the presence of watermark and easily modified by the intruders (Kaur et al., 2020). The second well-known technique is cryptography. Cryptography technique encrypts the secret message itself. This technique scrambles the secret message so that the intruders are unable to reveal the important information from the scrambled message (Al-Ataby and Al-Naima, 2010). The third technique is steganography. Steganography conceals the data into another media for the security purpose. This technique uses encoder to encode secret data into cover data. The decoder is used to decode the encoded message for secret message extraction. According to the nature of data, steganography techniques are broadly categorized into three classes namely, image, audio, and video (Kumar and Kumar, 2019). The main focus of this paper is image steganography due to the ease to implement in various domains.
Image steganography can be implemented in two different domains. These are spatial and frequency domains (Kumar et al., 2020). The former one modify the pixel values of an image through the computational techniques. Least-Significant Bit (LSB) technique is the well-known example of spatial domain technique (Ker, 2005). LSB manipulates the lowest-order bits of pixels in the given images to conceal the secret message. It methodically amends the statistical distribution of pixels of the image. The latter one transform the pixel values of the image. The transformed coefficients were processed to encode the secret message (Kumar and Kumar, 2017). The well-known frequency domain techniques are discrete cosine transform (DCT) and discrete wavelet transform (DWT). However, these techniques are not sufficient to handle the secret message in this modern era. To develop the efficient steganography technique, the concepts of modern technologies have been incorporated.
Recently, the deep learning techniques are widely used in steganography for strengthen the concealed secret data. The well-known deep learning technique is the convolutional neural network (CNN). Baluja (2017) utilized CNN for hiding the image into another cover image. An auto-encoder is used for image compression during the hiding process. Rehman et al. (2017) developed an encoder-decoder network to conceal the grey images into other images. In this paper, an encoder-decoder methodology is designed for the image steganography. This model can easily concealed the secret image inside a cover image and also extracted it with low distortion in quality. The main contributions of this paper are:
- 1.
An encoder decoder methodology is designed for image steganography with automatic feature selection.
- 2.
An extensive hyper-parameter tuning is done, which majorly lags in the previous research works.
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The designed methodology is evaluated on the publicly available datasets and tested through the well-known evaluation measures.