This paper presents a literature review of image steganography techniques in the spatial domain for last 5 years. The research community has already done lots of noteworthy research in image steganography. Even though it is interesting to highlight that the existing embedding techniques may not be perfect, the objective of this paper is to provide a comprehensive survey and to highlight the pros and cons of existing up-to-date techniques for researchers that are involved in the designing of image steganographic system. In this article, the general structure of the steganographic system and classifications of image steganographic techniques with its properties in spatial domain are exploited. Furthermore, different performance matrices and steganalysis detection attacks are also discussed. The paper concludes with recommendations and good practices drawn from the reviewed techniques.

Image steganography in spatial domain: A survey

Hiding clinical information in medical images

Information hiding is an active area of research where secret information is embedded in innocent-looking carriers such as images and videos for hiding its existence while maintaining their visual quality. Researchers have presented various image steganographic techniques since the last decade, focusing on payload and image quality. However, there is a trade-off between these two metrics and keeping a better balance between them is still a challenging issue. In addition, the existing methods fail to achieve better security due to direct embedding of secret data inside images without encryption consideration, making data extraction relatively easy for adversaries. Therefore, in this work, we propose a secure image steganographic framework based on stego key-directed adaptive least significant bit (SKA-LSB) substitution method and multi-level cryptography. In the proposed scheme, stego key is encrypted using a two-level encryption algorithm (TLEA); secret data is encrypted using a multi-level encryption algorithm (MLEA), and the encrypted information is then embedded in the host image using an adaptive LSB substitution method, depending on secret key, red channel, MLEA, and sensitive contents. The quantitative and qualitative experimental results indicate that the proposed framework maintains a better balance between image quality and security, achieving a reasonable payload with relatively less computational complexity, which confirms its effectiveness compared to other state-of-the-art techniques.

CISSKA-LSB: color image steganography using stego key-directed adaptive LSB substitution method

A reversible and secure patient information hiding system for IoT driven e-health

Information hiding scheme for digital images using difference expansion and modulus function

A high capacity image steganography using multi-layer embedding

An adaptive image steganography using AMBTC compression and interpolation technique

High-capacity adaptive steganography based on LSB and Hamming code

Aspect-level sentiment analysis aims at identifying the sentiment polarity of specific aspect words in a given sentence. Existing studies mostly use recurrent neural network (RNN) -based models. However, truncated backpropagation, gradient vanishing, and exploration problems often occur during the training process. To address these issues, this paper proposed a novel network with multiple attention mechanisms for aspect-level sentiment analysis. First, we apply the bidirectional encoder representations from transformers (BERT) model to construct word embedding vectors. Second, multiple attention mechanisms, including intra- and inter-level attention mechanisms, are used to generate hidden state representations of a sentence. In the intra-level attention mechanism, multi-head self-attention and point-wise feed-forward structures are designed. In the inter-level attention mechanism, global attention is used to capture the interactive information between context and aspect words. Furthermore, a feature focus attention mechanism is proposed to enhance sentiment identification. Finally, several classic aspect-level sentiment analysis datasets are used to evaluate the performance of our model. Experiments demonstrate that the proposed model can achieve state-of-the-art results compared to baseline models.

Mingwei Tang

Papers

A high capacity image steganography using multi-layer embedding

An adaptive image steganography using AMBTC compression and interpolation technique

High-capacity adaptive steganography based on LSB and Hamming code

A novel network with multiple attention mechanisms for aspect-level sentiment analysis

Graph convolutional network with multiple weight mechanisms for aspect-based sentiment analysis