الفهرس 
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Abstract
This thesis consists of eight chapters. In chapter one, we introduce an overview of the
thesis and its organization. In chapter two, the main definitions and also, explain the
different methods used in image encryption. In chapter three, We gave cryptanalysis of an
image encryption scheme, based on CNN and chaotic logistic map. We analyzed the security
of the scheme and we have shown that it can be broken with known/chosen plain-image and it
is insensitive to the change of the secret plain-image. Also We gave cryptanalysis of an
image encryption scheme based on elementary cellular automata. We pointed out there is a
simple condition makes a number of secret keys invalid. The scheme is insensitive to the
change of the plain-image. The scheme is not secured to the following three different classical
types of attacks: chosen plaintext, chosen ciphertext, and known plaintext. In the three
attacks, only a pair of (plaintext/ciphertext) is needed to break the image encryption scheme.
We proposed a one time key method for solving these problems and defeat cryptanalysis.
In chapter four, A new feedback Confusion/diffusion architecture encryption scheme based
on a 3’rd-order cellular neural network and affine transformation is proposed. It utilizes a
three order cellular neural network (CNN) as a generator of pseudo-random key stream
sequence. Furthermore, a novel affine transformation in permutation stage is proposed. In
order to produce good avalanche property and good sensitivity form cipher to plaintext, the
confusion and diffusion processes are controlled by rounds times’ key. The experimental
results show that the proposed encryption scheme appears a good resistance to brute force
attacks, known plaintext, and ciphertext attacks. Furthermore, the random-like nature of
cellular neural network is effectively spread into encrypted images. Simulation results and
analysis prove the validity and safety of the proposed algorithm. In chapter five, Based on
elementary cellular automata, a new image encryption algorithm is proposed, where a special
kind of periodic boundary cellular automata with unity attractors is used. The rule states used
in the encryption process are different from the states used in the decryption one. The key
stream is generated using the cellular neural network, and the control parameters are not
fixed, but determined by the plain-image. Thus, different plain images result in nonidentical
control parameters and distinct key streams. Also, the secret CA rules and states are
determined by the control parameters, and thus the rules and states also change with different
images. The security analysis for the proposed scheme has been presented. Simulation results
on some gray level images show that the proposed image encryption scheme has perfect
information concealing, and satisfies the properties of confusion and diffusion. And, some
other comparative results are also given to show that the proposed scheme’s security is
significantly enhanced, e.g., it can resist some known-plaintext and chosen-plaintext attacks
effectively, and the scheme is also more efficient in implementation, e.g., compared with
AES.In chapter six, A self-adaptive image encryption based on Memory Reversible Cellular
Automata (MRCA). The self-adaptive encryption is realized by using one half of image data
to encrypt the other half of the image mutually. We utilized cellular neural network system as
a generator of a pseudo-random key stream sequence. Simulation results on some gray level
images confirm that the proposed algorithm can be realized easily while achieving high
security level, high sensitivity and other randomness properties. In chapter seven, A new
cellular automata rule, we call ”Sum-rule”, is use for generating random numbers. Each
generated binary sequence is checked by the poker test. A designated five octal digits are
treated as a poker hand with new computations. Poker test is applied to Sum-rule, and on the
proposed sum rule with two demission cellular automata (2-DCA). Statistical tests show that
the sum rule passes a maximum number of NIST and FIPS tests compared to other PRNG
functions, where other tests not passed. When applying the sum rule with 2-DCA, the
generated sequence passed all statistical test suite for random and pseudorandom number
generator for cryptographic applications (NIST).
. In chapter eight, Conclusion and future work are presented.