# An Efficient Steganographic Protocol for WebP Files
###### Abstract
We may find different variants of this idea, using coefficient randomization [36], a specific frequency band [37], or matrix encoding [6]. DFT is rarely used compared to other transforms, but the literature mentions existing methods [20]. DWT is much more popular, probably because of better compression and less artifacts. This kind of transform decomposes the image into a low-frequency signal and high-frequency details in horizontal, vertical, and diagonal directions. There are methods for both grayscale [38] and color images [39; 40]. The frequency approach is useful in steganography, as the secret data are scattered among pixels and therefore difficult to wipe without degrading the quality of the container. We may also mention other domains useful for data hiding, like eigenvectors [41]. The main goal of these algorithms is to achieve high robustness, as it is difficult to remove the payload without the destruction of important areas of the photograph. There are also attacks aimed at frequency-domain methods, based on coefficient analysis [42] or classification with neural networks [43]. Thanks to development of neural networks and deep learning, a new category has found its place in image steganography. Deep neural networks have been used to select the embedding area for the LSB method and to scatter data between all of the available bits, while another network served the purpose of secret extracting [44]. Other types of architectures also found application in steganography. The authors of [45] proposed two networks (U-Net-based encoder and decoder) for hiding and recovering secret data, both working on color photographs. An interesting approach uses the style modification technique [46]. This algorithm takes three input pictures: cover, style, and secret. The resulting image resembles the carrier, but contains concealed data and is also drawn in the requested style. Other examples of steganography include combinations with cryptography [47; 48], watermarking [27; 49] or secret sharing [50; 51; 52], multi-secret steganography used for concealing more than one message in a single container [53], techniques that do not require a predefined medium but generate the carrier from scratch [54; 55], format-specific methods destined for particular files [36; 56; 57], the creation of subliminal channels in existing schemes to achieve better undetectability [58; 59], systems that use biometric authentication for data hiding [60; 61], combining various techniques to obtain secure data transmission in telemedicine applications [62; 63], and many more. Among container modification techniques, spatial-domain algorithms focus on uncompressed or lossless compressed images, like BMP or PNG. On the other hand, the majority of transform-domain methods operate on JPEG images. There are also some proposals of data hiding in GIF images. The WebP format had not aroused much interest in the information hiding community. Although it was introduced years ago, its popularity was not very high compared to other, well-established image files. As can be seen in related works, there is a shortage of papers concerning WebP steganography. This is the reason for this study, to present techniques appropriate for these images. In recent years, the presence of WebP on the internet became visible, so the time has come for this versatile format to take its place among other carriers suitable for steganography.