A new deep steganographic technique for hiding several secret images in one cover

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol. 14, No. 3, June 2025, pp. 2210~2219
ISSN: 2252-8938, DOI: 10.11591/ijai.v14.i3.pp2210-2219  2210
Journal homepage: http://ijai.iaescore.com
A new deep steganographic technique for hiding several secret
images in one cover
Mohamed Htiti1
, Aziza El Ouaazizi2
, Ismail Akharraz3
, Abdelaziz Ahaitouf1
1
Laboratory of Engineering Sciences (LSI), Faculty of Taza Polydiscipline, University of Sidi Mohamed Ben Abdellah, Fez, Morocco
2
Laboratory of Artificial Intelligence, Data Sciences and Emergent Systems (LIASSE), National School of Engineers (ENSA),
University of Sidi Mohamed Ben Abdellah, Fez, Morocco
3
Laboratory of Mathematical and Informatics Engineering, University of Ibnou Zohr, Agadir, Morocco
Article Info ABSTRACT
Article history:
Received Sep 16, 2024
Revised Jan 5, 2025
Accepted Jan 27, 2025
Deep learning has been integrated with image steganography to enhance
steganographic security by automatically acquiring the ability to hide
information. The issue with current models is that if the cover image is
accessible, it is possible to expose the hidden information by simply
calculating the differences between the cover image and the steganographic
image. This paper introduces a novel image steganography model that
utilizes convolutional neural network (CNN) to enhance the dissimulation
and extraction capabilities. Specifically, we propose a model that hides two
images in a single cover image. Before being hidden within the cover image,
a random pixel image is generated and combined with the real secret image.
Experimental results show that our proposed method is more effective and
relevant.
Keywords:
Convolutional neural network
Deep learning
Deep steganography
Digital data security
Image steganography This is an open access article under the CC BY-SA license.
Corresponding Author:
Mohamed Htiti
Laboratory of Engineering Sciences (LSI), Faculty of Taza Polydiscipline
University of Sidi Mohamed Ben Abdellah
Fez, Morocco
Email: mohamed.htiti1@usmba.ac.ma
1. INTRODUCTION
Information security relies on three fundamental categories: watermarking, cryptography, and
steganography. Watermarking is mainly used to protect copyright and create electronic signatures [1] while
cryptography is essential for maintaining the confidentiality, integrity, and authentication of data in public
communications, offering strong protection [2]. Steganography involves hiding information within other
content to transmit data without drawing attention to the concealed message [3]. Images are commonly used
in steganography due to their high capacity to contain data, their popularity, and the possibility of hiding
information invisibly from the human eye. Every digital image, especially high-resolution ones, contains
numerous pixels and color components, enabling bits to be modified without visibly altering the image. In
addition, there is a high degree of correlation between adjacent pixels in an image, enabling data to be hidden
imperceptibly by slightly altering the values of individual pixels. Images also offer data redundancy and are
highly resistant to certain compressions, as in lossless formats such as PNG, enabling hidden data to be
recovered even after manipulation. Their omnipresence in digital communications and their easy
transmission make them an ideal medium for discreetly hiding information. In the context of images,
steganography involves embedding secret information in a cover image. An image steganography algorithm
consists of two parts. The first part is used to hide a confidential message inside the cover image. The second

Int J Artif Intell ISSN: 2252-8938 
A new deep steganographic technique for hiding several secret images in one cover … (Mohamed Htiti)
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part is an extraction algorithm designed to recover of the secret message from the stego image (the cover
image containing the secret) [4].
Currently, there are two categories of steganographic algorithms, namely classical techniques and
machine learning techniques. In the first categories, we find domain spatial techniques where the secret
information bits are embedded directly in the pixels of the cover image. Several research projects have been
carried out to improve the techniques of this approach, including the least significant bit (LSB) technique
[5]–[10], where the secret bits are generally hidden in the LSB of the cover pixels. Another technique called
pixel value differencing (PVD) involves discreetly altering the pixel values of an image to encode the hidden
information. This is achieved by making small modifications to the disparity values of adjacent pixels to
encode the desired data bits [11]–[14]. Another important type of classical technique is the transform domain.
The initial step is to transform the cover image into the frequency domain, and the secret information is then
hidden in the wavelet. Several research projects have been carried out to improve the performance of this
approach. The discrete cosine transform (DCT) technique has undergone several improvements and
implementations in this work, [15]–[18]. Similarly, the discrete wavelet transform (DWT) technique has also
been explored [19]. In the second category, which utilizes artificial intelligence, existing research shows
promise for information security because it preserves the visual and statistical properties of cover images
with effective hiding of secret information. Indeed, unlike traditional steganography, which often focuses on
modifying the characteristics or pixels of the cover image to incorporate secret data, deep steganography uses
neural networks to generate images containing hidden information. This offers potentially more robust and
sophisticated concealment capabilities, as neural networks can learn complex patterns and representations.
Deep steganography takes advantage of the strengths of machine learning, enabling the process of hiding and
extracting confidential data to be learned automatically.
Several artificial intelligence tools have been employed by researchers in the field of deep
steganography, such as genetic algorithms [20], generative adversarial networks [21], and convolutional
neural network (CNN) autoencoder; Baluja [22] who use deep neural networks to hide a color image inside
another image of the same size, demonstrating the ability of the deep learning method in image hiding.
Baluja's problem is that it is possible for an attacker to obtain the original cover image (C) without the
embedded secret image. In this case, it becomes possible to partially reveal the secret image by comparing
the original cover image (C) with the sent image (C') by plotting their difference |C-C’|. Baluja [23], while
suggesting concealing multiple images within a single image, initially trained his model to embed one image
in one cover. The model identifies the optimal locations for embedding the hidden information and decides
how to compress and represent it. Subsequently, the author proposed utilizing the resulting model to
compress and conceal two images within one cover.
In this paper, we propose a new architecture that hides two secret images rather than just one in the
cover image. After training our model, it should be able to hide two secret images within a single cover
image. Before implementing this, we suggest a technique to enhance the solution's performance by randomly
generating a fake secret image and embedding it alongside the real secret in the cover image. The rest of the
paper is structured as follows: section 2 describes the problematic aspects related to the topic and highlights
our specific contributions. Results and discussions are presented in section 3. Finally, in section 4, we give
the conclusion and perspectives of our study.
2. PROPOSED METHOD
Baluja [23] has succeeded in hiding an original color image (S) inside another cover image (C), the
result being the stegano image (C'). However, it is possible for an attacker to obtain the original cover image
(C) without the embedded secret image. In this case, it becomes possible to partially reveal the secret image
by comparing the original cover photo (C) with the sent photo (C') by calculating their difference. An
example of Baluja's model is shown in Figure 1, which illustrates how the difference between (C) and (C')
can reveal the secret. Column 3 represents the Stegano image, which is simply the secret image displayed in
column 2 and embedded in the cover image in column 1. The extracted secret is shown in column 4. By
comparing the cover image (C) and the stegano image (C') in column 5, we can see that the secret hidden in
the stego image is visible. When a steganalyst obtains images C and C', he can determine the secret by
calculating the difference between them |C-C’|.
One solution, suggested by Sharma et al. [24], is to integrate a block permutation onto the secret
image before incorporating it into the learning process. Figure 2 shows an example of block permutation
applied to an image. Since he considered the permutation he performed to be a form of cryptography, he
applied cryptographic criteria to his results. In addition, his model is trained on a database of permuted
images, which further increases the computational complexity. In other words: i) during the prediction stage:
using this model implies using both the permutation and dissimilation algorithms and ii) during the reception

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Int J Artif Intell, Vol. 14, No. 3, June 2025: 2210-2219
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stage: it is necessary to use the extraction algorithm and the permutating canceling algorithm, as shown in
Figure 3.
Figure 1. Results using the Baluja’s model

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Figure 2. Example of Sharma permutation
Figure 3. Sharma model
We propose hiding several secret images in a single cover image rather than including a permutation
or cryptographic layer that complicates the computation. To enhance dissimulation quality, we propose a
novel CNN-based model that trained to hide a randomly generated image and the secret image in one cover
image. This approach introduces complexity and masks the secret image features, making it more challenging
for attackers to detect or extract.
Figure 4 illustrates the general principle of the proposed architecture. The structure of the network in
question resembles that of autoencoders. In general, encoders attempt to make an output that is very close to
the input after a number of changes. This process enables them to learn the fundamental characteristics of the
input images. However, in our case, instead of just reproducing images, the network has the additional task of
hiding two images while simultaneously generating another image (stego image). The preparation layer adds
Gaussian noise to (the secret S+random image). This layer prevents the model from storing information in
LSB bits. Next, we concatenate the input images with the cover C. The second layer consists of the hiding
network, which takes the output of the preparation layer as input to generate the stego image C’.
This network comprises 10 convolution layers, each made up of three parallel sub-layers with
128 filters of sizes (3×3), (4×4), and (5×5) respectively. The third layer is the revelation network, which takes
the stego image exclusively as input. This network is responsible for removing the cover image to reveal the
secret image S’.
The model was trained using Python 3 Google Compute Engine on the Google Colab platform,
which provides access to additional resources, including graphics processing unit (GPU) and random access
memory (RAM). Google Colab also provides a JupyterLab environment in which model development can
take place. This environment also provides access to all the essential libraries for artificial intelligence and
machine learning, including Keras, Matplotlib, Numpy, and Scipy.
The Flickr30k database was used to train the model. The images in the dataset were reduced to
256×256 according to our training model because their sizes were irregular throughout the dataset. A total of
1000 covers, 1000 secrets, and 1000 random images were used in the training process.

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Figure 4. Proposed architecture
3. RESULTS AND DISCUSSION
In most cases, imperceptibility can be determined by comparing the pixel values of the original
image with those of the stego image. The result is shown in Figure 5; column 1 shows the cover images,
columns 2 and 3 represent the true secrets and random images respectively. Columns 4 and 5 contain the
stegano image and the extracted secret image. Column 6 shows the |C-C'| difference. We can see that there is
no trace of the secret in the images in the “Diff cover” column. Visually, our model is more successful than
Baluja's. Reliable tools are crucial for assessing the quality and imperceptibility of steganographic images.
Various indicators are used for this purpose, including peak signal-to-noise ratio (PSNR), universal image
quality index (UIQI), blind/referenceless image spatial quality evaluator (BRISQUE) score, and structural
similarity index measure (SSIM). Here's a detailed presentation of each of these indicators:
3.1. Peak signal-to-noise ratio
PSNR is an indicator commonly used to assess the level of noise present in the pixels of a
steganographic image, in order to measure its imperceptibility. Steganographic images of excellent quality
have a PSNR value of 40 dB or more, while those with a value of less than 30 dB are considered to be of
inferior quality. PSNR is calculated using logarithmic values of mean square error (MSE) [25] and 𝐼𝑚𝑎𝑥
2
represents the highest pixel value in the image.
𝑃𝑆𝑁𝑅 = 10 log10 (
𝐼𝑚𝑎𝑥
2
𝑀𝑆𝐸
) (1)
3.2. Universal image quality index
This index is used to evaluate the variations present in the steganographic image compared to the
original image. This method divides the image comparison into three distinct parts: i) luminance comparison
(LC), ii) contrast comparison (CC), and iii) structural comparison (SC). The UIQI index varies between
-1 and 1, the best value being 1 [26]:
𝐿(𝑋, 𝑌) =
2𝜇𝑋𝜇𝑌
𝜇𝑋2+𝜇𝑌2
(2)
𝐶(𝑋, 𝑌) =
2𝜎𝑋𝜎𝑌
𝜎𝑋2+𝜎𝑌2
(3)
𝑆(𝑋, 𝑌) =
2𝜎𝑋𝑌
𝜎𝑋+𝜎𝑌
(4)
UIQI⁡(𝑋, 𝑌) = 𝐿(𝑋, 𝑌) ∗ 𝐶(𝑋, 𝑌) ∗ 𝑆(𝑋, 𝑌) (5)

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Where X represents the cover image, Y represents the stego image, 𝜇𝑋 represents the mean value of the X
matrix, 𝜇Y represents the mean value of the Y, 𝜎X represents the standard deviation of the X matrix,
𝜎Y represents the standard deviation of the Y matrix, and 𝜎XY represents the covariance between the X and Y
matrices.
3.3. Blind/referenceless image spatial quality evaluator score
The BRISQUE score [27] is a statistical measurement of the natural scene, excluding reference
images such as PSNR and SSIM. It ranges from 0 to 100, with the best score being the lowest. The score is
calculated using the support vector regression (SVR) model and difference mean opinion score (DMOS). It is
widely used in image steganography research.
3.4. Structural similarity index
The SSIM is a metric used to evaluate the imperceptibility of steganographic images. Unlike PSNR,
which relies on a summation method, SSIM focuses on three key factors: luminance, contrast, and structure,
providing a more comprehensive assessment of image quality. In red, green, and blue (RGB) color images,
the SSIM can be mathematically defined using (6). The first component, 𝐿(𝑖𝑚, im′), is responsible for
comparing the luminance levels between two images, im and im’. This luminance comparison reaches its
maximum value of 1 when the luminance of both images is identical. The maximum value of 𝑐(𝑖𝑚, 𝑖𝑚′) is 1,
which occurs when the contrasts of the two images, calculated based on their standard deviations (σ), are
equal. The third component, 𝑆(𝑖𝑚, im′), compares the structural similarity between two images im and im’
based on their correlation coefficient. The maximum possible value for SSIM is 1, with the overall range of
SSIM values falling between 0 and 1. To prevent division by zero, constant values C1, C2, C3 are introduced
in the formula. It is recommended to use the values C1=(0.01×255)2
, C2=(0.03×255)2
, and C3=C2/2 as the
default value [28].
SSIM⁡(𝑖𝑚, im′) = 𝑙(𝑖𝑚, im′)𝑐(𝑖𝑚, im′)𝑠(𝑖𝑚, im′) (6)
𝑙(𝑖𝑚, 𝑖𝑚′) =
2𝜇𝑖𝑚𝜇𝑖𝑚′+𝐶1
𝜇𝑖𝑚
2 +𝜇
𝑖𝑚′
2 +𝐶1
(7)
𝑐(𝑖𝑚, 𝑖𝑚′) =
2𝜎𝑖𝑚𝜎𝑖𝑚′+𝐶2
𝜎𝑖𝑚
2 +𝜎
𝑖𝑚′
2 +𝐶2
(8)
𝑠(𝑖𝑚, 𝑖𝑚′) =
𝜎𝑖𝑚𝑖𝑚′+𝐶3
𝜎𝑖𝑚𝜎𝑖𝑚′+𝐶3
(9)
𝜇𝑖𝑚 =
∑𝑀
𝑥=1 ∑𝑁
𝑦=1 ∑𝑂
𝑧=1 𝑖𝑚𝑥𝑦𝑧
𝑀𝑁𝑂
(10)
𝜎𝑖𝑚
2
=
∑𝑀
𝑥=1 ∑𝑁
𝑦=1 ∑𝑂
𝑧=1 (𝑖𝑚𝑥𝑦𝑧−𝜇𝑖𝑚)
2
𝑀𝑁𝑂
(11)
𝜎𝑖𝑚,𝑖𝑚′ =
∑𝑀
𝑥=1 ∑𝑁
𝑦=1 ∑𝑂
𝑧=1 (𝑖𝑚𝑥𝑦𝑧−𝜇𝑖𝑚)(𝑖𝑚𝑥𝑦𝑧
′ −𝜇𝑖𝑚′)
𝑀𝑁𝑂
(12)
𝜇𝑖𝑚′ =
∑𝑀
𝑥=1 ∑𝑁
𝑦=1 ∑𝑂
𝑧=1 𝑖𝑚𝑥𝑦𝑧
′
𝑀𝑁𝑂
(13)
𝜎𝑖𝑚′
2
=
∑𝑀
𝑥=1 ∑𝑁
𝑦=1 ∑𝑂
𝑧=1 (𝑖𝑚′
𝑥𝑦𝑧−𝜇𝑖𝑚′)
2
𝑀𝑁𝑂
(14)
Where M and N represent the image resolution and O denotes the number of image channels.
After conducting validation tests on both our model and Baluja's model, we compiled the results in
Table 1. Table 1 provides a detailed comparison between the two approaches, highlighting the strengths and
weaknesses of each. It allows for a clearer understanding of how the models perform across various metrics.
We can see that our model is much better than the Baluja one. Indeed, the proposed model has slightly higher
PSNR, SSIM, and UIQI values than the Baluja model, indicating better preservation of image quality. In
addition, our model has significantly lower BRISQUE scores, indicating better sego image quality. Overall,
the proposed model offers better image quality preservation.

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Figure 5. Results of the proposed model
Table 1. Comparative analysis of our model and Baluja's model based on validation tests
Metrics Baluja’s model Our model
PSNR 83.6284 84.8693
SSIM 0.92224 0.94717
UIQI 78.62901 82.43392
BRISQUE_SCORE 19.73211 7.39974
In addition, our model can embed two secret images into a cover image, as demonstrated in
Figure 6. It can also extract these images in real-time. Figure 6 presents two examples illustrating the success
of the hiding and extraction process. In the first column, the cover image is shown, followed by secret images
in columns two and three. The fourth column displays the stego image (the cover image with the hidden
secret images), while columns five and six show the extracted secret images.
Figure 6. Example of hiding two secrets in one cover

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4. CONCLUSION
In this paper, we present a new steganographic model for hiding multiple secret images in a cover
image. To implement our model we combine a real secret image with a randomly generated pixel image. This
approach enables better dissimulation of the secret image features in the difference between the original
cover image and the stego image. Our contribution offers promising possibilities for hiding sensitive
information while maintaining the natural appearance of the cover image. This work can be exploited in the
field of intelligent advertising used in football match panels, which are used to display different
advertisements according to the needs of spectators and local businesses.
ACKNOWLEDGEMENTS
The author would like to express special appreciation to the Laboratory of Engineer Sciences for
providing the necessary facilities and resources.
FUNDING INFORMATION
Authors state no funding involved.
AUTHOR CONTRIBUTIONS STATEMENT
This journal uses the Contributor Roles Taxonomy (CRediT) to recognize individual author
contributions, reduce authorship disputes, and facilitate collaboration.
Name of Author C M So Va Fo I R D O E Vi Su P Fu
Mohamed Htiti ✓ ✓ ✓ ✓ ✓ ✓
Aziza El Ouaazizi ✓ ✓ ✓ ✓ ✓
Ismail Akharraz ✓ ✓ ✓ ✓ ✓
Abdelaziz Ahaitouf ✓ ✓ ✓ ✓ ✓
C : Conceptualization
M : Methodology
So : Software
Va : Validation
Fo : Formal analysis
I : Investigation
R : Resources
D : Data Curation
O : Writing - Original Draft
E : Writing - Review & Editing
Vi : Visualization
Su : Supervision
P : Project administration
Fu : Funding acquisition
CONFLICT OF INTEREST STATEMENT
The authors declare that they have no known competing financial interests or personal relationships
that could have appeared to influence the work reported in this paper. Authors state no conflict of interest.
INFORMED CONSENT
Informed consent was obtained from all individuals included in this study.
DATA AVAILABILITY
The data that support the findings of this study are available on request from the corresponding
author, [M. H.].
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BIOGRAPHIES OF AUTHORS
Mohamed Htiti received the B.Sc. degree in nuclear physics from Sidi
Mohamed Ben Abdallah University, in 2002. Received the M.Sc. degree in physics and
nuclear techniques from Cadi Ayyad University, in 2005. Received the B.Sc. degree in IT
Engineering from Faculty of Taza Polydiscipline, University of Sidi Mohamed Ben Abdellah,
in 2020. Received a specialized master's degree in intelligent and mobile systems from the
Faculty of Taza Polydiscipline, Morocco, in 2023. Now he is a Ph.D. student registered in the
LSI Laboratory of Engineer Sciences of the Faculty of Taza Polydiscipline, University of Sidi
Mohamed Ben Abdellah, Fez, Morocco. His research interests include cryptography,
steganography, deep learning, and image processing. He can be contacted at email:
mohamed.htiti1@usmba.ac.ma.

2219
Aziza El ouaazizi received her Ph.D. at University of Sidi Mohamed Ben
Abdellah in 2000. After working as a Professor in Technical High School of Fes (2001), she is
currently working for Professor in the Informatics at University of Sidi Mohamed Ben
Abdallah, Fez. She is also a permanent member of Artificial Intelligence Data Sciences and
Emergent Systems Laboratory and an associate member of Engineering Science Laboratory.
Her research interests include machine and deep learning, artificial vision and image
processing, pattern recognition, data analysis, evolutionary algorithms, and their applications.
She can be contacted at email: aziza.elouaazizi@usmba.ac.ma.
Ismail Akharraz received his master and Ph.D. degrees in number theory from
the University of Sidi Mohmed Ben Abdellah Fez, Morocco, in 2000. From 2003 to 2020, he
was at the University of Sidi Mohamed Ben Abdellah Fez, as permanent professor and
permanent member of the Laboratory of Engineering Sciences. From 2021, he joined
University of Ibn Zohr, Agadir Morocco, as a permanent professor and permanent member of
the Laboratory of Mathematical and Informatic Engineering. His current areas of research are
error-correcting codes and cryptography, intelligent systems, and recommendation systems.
He can be contacted at email: ismail.akharraz@usmba.ac.ma.
Abdelaziz Ahaitouf received his physics diploma at the University of Moulay
Ismail Meknes. From 1995 to 1999, he received his M.D. and Ph.D. degrees in electronics
from the University of Metz, France. In 2000, he worked in a postdoctoral position on the
development of a SOI fully and partially depleted process at the Swiss Federal Institute of
Technology (EPFL), Switzerland. From 2003, he joined the University of Sidi Mohamed Ben
Abdellah Fez, Morocco where he is teaching in the field of electronic and IC manufacturing.
He is currently working in the field of microelectronics, electrical device characterization,
intelligent systems, and LDPC encoding/decoding. He can be contacted at email:
abdelaziz.ahaitouf@usmba.ac.ma.

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