# Enhanced Steganography for High Dynamic Range Images with Improved Security and Capacity
## 1 Introduction  
With the increasing prevalence of the Internet, the security of communication channels cannot be guaranteed due to the risk of eavesdropping. In cases where transmitted data is confidential, such as military intelligence or medical histories, encryption is a common method used to protect sensitive information. However, encrypted data is often more easily detected by eavesdroppers due to its complex nature compared to unencrypted data. Consequently, steganography [1; 2; 3; 4] has emerged as a crucial technique for enhancing information security. Steganography, in contrast to cryptography, is concerned with concealing communication [5]. The digital objects typically used in information security include images, texts, videos, and others. Among these, digital images are frequently employed due to their redundancy and ubiquitous use in daily life. The resulting images after secret data is embedded are known as stego-images, while those without such data are called cover images. The evaluation of a steganography technique involves three primary benchmarks: the capacity to conceal secret data, the visual quality of the stego-images, and the security, which refers to the detection of steganalysis [2]. A steganography scheme should therefore aim to maximize the amount of data that can be embedded in stego-images while minimizing the distortion between cover images and stego-images and avoiding suspicion of the presence of any secret information. Recently, there has been a growing interest in deep learning-based image steganography techniques, and this has involved discussions on existing methods and has provided valuable insight [3]. With advances in imaging technology, higher-quality images that better resemble real-world scenes are being sought after. This has led to the development of a new format for digital images, which can be divided into two categories: low-dynamic-range (LDR) images and high-dynamic-range (HDR) images. An LDR image is a traditional image that is composed of three color channels, red (R), green (G), and blue (B), each represented using 8 bits, for a total of 24 bits. In contrast, an HDR image represents each R, G, or B channel using 32 bits, for a total of 96 bits. Unlike LDR images, the dynamic range of HDR images can be recorded using 96 bits, providing greater detail in even the darkest and brightest regions beyond what the human eye can perceive. HDR images can be generated using HDR cameras or by combining multiple LDR images with different exposures [6]. Due to the high cost of storing and transmitting 96-bit HDR images, researchers have developed various compact formats for HDR images, including RGBE [7], LogLuv [8], OpenEXR [9], and JPEG-XT [10]. While steganography schemes for LDR images have reached relative maturity, the research on steganography for HDR images is still in its early stages. HDR imaging techniques have been developed to capture the full range of color and light that the human eye can perceive in the real world. As a result, HDR media contain significantly richer content than their LDR counterparts and are therefore much more valuable. In fact, most cameras and smartphones currently available on the market are capable of capturing HDR images. Thus, it is crucial to develop proper tools for protecting the intellectual property of digital HDR media from the early stages of technology development. There are two primary categories of data hiding or steganography algorithms designed for HDR images. The first type aims to achieve high-capacity data hiding [11; 12] by conveying a significant amount of secret messages, but at the expense of producing a stego-image with substantial distortion. These algorithms are currently state-of-the-art and offer an embedding rate of at least 5 bits per pixel. The second type of algorithm aims to achieve a high image quality in data hiding [4; 13; 14; 15; 16; 17].