Reversible Data Hiding in Encrypted Images Using Median Edge Detector and Two’s Complement

Abstract

With the rapid development of cloud storage, an increasing number of users store their images in the cloud. These images contain many business secrets or personal information, such as engineering design drawings and commercial contracts. Thus, users encrypt images before they are uploaded. However, cloud servers have to hide secret data in encrypted images to enable the retrieval and verification of massive encrypted images. To ensure that both the secret data and the original images can be extracted and recovered losslessly, researchers have proposed a method that is known as reversible data hiding in encrypted images (RDHEI). In this paper, a new RDHEI method using median edge detector (MED) and two’s complement is proposed. The MED prediction method is used to generate the predicted values of the original pixels and calculate the prediction errors. The adaptive-length two’s complement is used to encode the most prediction errors. To reserve room, the two’s complement is labeled in the pixels. To record the unlabeled pixels, a label map is generated and embedded into the image. After the image has been encrypted, it can be embedded with the data. The experimental results indicate that the proposed method can reach an average embedding rate of 2.58 bpp, 3.04 bpp, and 2.94 bpp on the three datasets, i.e., UCID, BOSSbase, BOWS-2, which outperforms the previous work.

An Ensemble Learning Approach for Reversible Data Hiding in Encrypted Images with Fibonacci Transform

“…RDH schemes are also explored for use with different image datasets, such as natural images [42], medical images [43], etc. Though many RDH schemes have been described in the literature [44,45], RDH techniques which operate in an encrypted medium [16,46] have become widely accepted due to their wide range of practical applications pertaining to the security of the cover medium.…”

“…RDH can be implemented on a natural image [12][13][14][15] or over an encrypted image [16][17][18]. Drawbacks of using natural images as the cover image include:…”

Intra-Block Correlation Based Reversible Data Hiding in Encrypted Images Using Parametric Binary Tree Labeling

“…Yin et al [26] used the AMBTC technique for data embedding, although not in the RDHEI domain. Further, it has been observed that the AMBTC technique is a popular lossy compression technique which has been widely used in data hiding [14,[36][37][38]. As far as working method of [33] is concerned, Su et al's technique first scrambles and then compresses the host image into triplets in a block-wise manner, where each triplet includes two quantization level (high & low) and a bitmap.…”

An enhanced reversible image steganography using distributive Tian's method

“…The multimedia type includes video files of any format, audio files of any format, and image files. [1][2][3][4][5][6] The objective of this steganography technique is to share the information in a secret way that ensures privacy over the internet. This can also lead to a few advantages, including copyright preservation, detection of tampering image authentication and so on.…”

“…Steganography is a method of hiding information into any digital media files, or in simple terms, it is known as multimedia. The multimedia type includes video files of any format, audio files of any format, and image files 1‐6 . The objective of this steganography technique is to share the information in a secret way that ensures privacy over the internet.…”