## 1 Introduction  
With the evolution of the Internet, one of the major concerns is to secure communicated information. Generally, information security is achieved by encryption (cryptography) and information hiding (steganography). In cryptography, information is encoded such that only authorized users can access it. In steganography, the information is concealed in a digital medium such that no one can notice whether the digital medium has the information inside it or not [1].  
In recent years, steganography techniques have been employed in various digital mediums, i.e., image, audio, video, and network protocols to achieve secret communication. Similarly, it is employed in various applications; [PERSON] and [PERSON] used steganography to secure stored data elements in the cloud [2]. [PERSON] et al. [3] employed steganography for secret sharing and authentication purposes. [PERSON] et al. used steganography for sending data from a mobile to the cloud and vice versa [4]. [PERSON] et al. [5] used steganography process to secure localization of nodes in wireless sensor networks.  
Usually, the objectives of image steganography consist of listed key factors, i.e., visual imperceptibility, embedding capacity, robustness, and security. Visual imperceptibility is considered as the resistance of the human visual system to identify the changes in the stego-image that can be measured by the peak signal to noise ratio (PSNR). Embedding capacity refers to the amount of concealed information inside a cover image. Robustness means extracting the original concealed information from stego-image regardless of various types of processing, i.e., cropping, scaling and filtering. Moreover, security against steganalysis and the integrity of secret data can be added to these objectives [1]. Ideally, a steganography algorithm should be able to achieve larger embedding capacity with high visual imperceptibility while maintaining the security and integrity of secret data.  
In this study, we will discuss the well-known image-based steganography techniques that deal with the least significant bit (LSB) substitution of the cover image pixels to hide secret data. LSB-based image steganography methods embed secret data in the least significant bits of the image pixel value. Usually, achieving the larger embedding capacity in LSB methods requires a large number of bits modification that can degrade the visual quality. Moreover, it relies on the fact that replacing one or more of the last 1-4 bits of cover image's pixels is imperceptible to the human visual system, but some statistical tests could detect their replacement in appropriate locations [6]. Therefore, one needs to design an embedding method requiring a lower number of bits modifications per pixel during the embedding process; the method should achieve a larger embedding capacity. In this study, the proposed method successfully achieves these objectives.  
The organization of the paper is as follows. The literature review of LSB-based steganography methods is discussed in Section 2. Section 3 presents the proposed steganography method with discrete algorithm steps and explanations of embedding and extraction procedure. Section 4 is dedicated to performance analysis (embedding capacity, visual imperceptibility, and statistical security) with a comparison of existing LSB-based steganography techniques. Finally, the conclusions and future direction of the research are discussed in Section 5.