624 research outputs found
3D Textured Model Encryption via 3D Lu Chaotic Mapping
In the coming Virtual/Augmented Reality (VR/AR) era, 3D contents will be
popularized just as images and videos today. The security and privacy of these
3D contents should be taken into consideration. 3D contents contain surface
models and solid models. The surface models include point clouds, meshes and
textured models. Previous work mainly focus on encryption of solid models,
point clouds and meshes. This work focuses on the most complicated 3D textured
model. We propose a 3D Lu chaotic mapping based encryption method of 3D
textured model. We encrypt the vertexes, the polygons and the textures of 3D
models separately using the 3D Lu chaotic mapping. Then the encrypted vertices,
edges and texture maps are composited together to form the final encrypted 3D
textured model. The experimental results reveal that our method can encrypt and
decrypt 3D textured models correctly. In addition, our method can resistant
several attacks such as brute-force attack and statistic attack.Comment: 13 pages, 7 figures, under review of SCI
A novel conservative chaos driven dynamic DNA coding for image encryption
In this paper, we propose a novel conservative chaotic standard map-driven
dynamic DNA coding (encoding, addition, subtraction and decoding) for the image
encryption. The proposed image encryption algorithm is a dynamic DNA coding
algorithm i.e., for the encryption of each pixel different rules for encoding,
addition/subtraction, decoding etc. are randomly selected based on the
pseudorandom sequences generated with the help of the conservative chaotic
standard map. We propose a novel way to generate pseudo-random sequences
through the conservative chaotic standard map and also test them rigorously
through the most stringent test suite of pseudo-randomness, the NIST test
suite, before using them in the proposed image encryption algorithm. Our image
encryption algorithm incorporates a unique feed-forward and feedback mechanisms
to generate and modify the dynamic one-time pixels that are further used for
the encryption of each pixel of the plain image, therefore, bringing in the
desired sensitivity on plaintext as well as ciphertext. All the controlling
pseudorandom sequences used in the algorithm are generated for a different
value of the parameter (part of the secret key) with inter-dependency through
the iterates of the chaotic map (in the generation process) and therefore
possess extreme key sensitivity too. The performance and security analysis has
been executed extensively through histogram analysis, correlation analysis,
information entropy analysis, DNA sequence-based analysis, perceptual quality
analysis, key sensitivity analysis, plaintext sensitivity analysis, etc., The
results are promising and prove the robustness of the algorithm against various
common cryptanalytic attacks.Comment: 29 pages, 5 figures, 15 table
A DNA Based Colour Image Encryption Scheme Using A Convolutional Autoencoder
With the advancement in technology, digital images can easily be transmitted
and stored over the Internet. Encryption is used to avoid illegal interception
of digital images. Encrypting large-sized colour images in their original
dimension generally results in low encryption/decryption speed along with
exerting a burden on the limited bandwidth of the transmission channel. To
address the aforementioned issues, a new encryption scheme for colour images
employing convolutional autoencoder, DNA and chaos is presented in this paper.
The proposed scheme has two main modules, the dimensionality conversion module
using the proposed convolutional autoencoder, and the encryption/decryption
module using DNA and chaos. The dimension of the input colour image is first
reduced from N M 3 to P Q gray-scale image using the
encoder. Encryption and decryption are then performed in the reduced dimension
space. The decrypted gray-scale image is upsampled to obtain the original
colour image having dimension N M 3. The training and
validation accuracy of the proposed autoencoder is 97% and 95%, respectively.
Once the autoencoder is trained, it can be used to reduce and subsequently
increase the dimension of any arbitrary input colour image. The efficacy of the
designed autoencoder has been demonstrated by the successful reconstruction of
the compressed image into the original colour image with negligible perceptual
distortion. The second major contribution presented in this paper is an image
encryption scheme using DNA along with multiple chaotic sequences and
substitution boxes. The security of the proposed image encryption algorithm has
been gauged using several evaluation parameters, such as histogram of the
cipher image, entropy, NPCR, UACI, key sensitivity, contrast, etc. encryption
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