529 research outputs found
A Novel Latin Square Image Cipher
In this paper, we introduce a symmetric-key Latin square image cipher (LSIC)
for grayscale and color images. Our contributions to the image encryption
community include 1) we develop new Latin square image encryption primitives
including Latin Square Whitening, Latin Square S-box and Latin Square P-box ;
2) we provide a new way of integrating probabilistic encryption in image
encryption by embedding random noise in the least significant image bit-plane;
and 3) we construct LSIC with these Latin square image encryption primitives
all on one keyed Latin square in a new loom-like substitution-permutation
network. Consequently, the proposed LSIC achieve many desired properties of a
secure cipher including a large key space, high key sensitivities, uniformly
distributed ciphertext, excellent confusion and diffusion properties,
semantically secure, and robustness against channel noise. Theoretical analysis
show that the LSIC has good resistance to many attack models including
brute-force attacks, ciphertext-only attacks, known-plaintext attacks and
chosen-plaintext attacks. Experimental analysis under extensive simulation
results using the complete USC-SIPI Miscellaneous image dataset demonstrate
that LSIC outperforms or reach state of the art suggested by many peer
algorithms. All these analysis and results demonstrate that the LSIC is very
suitable for digital image encryption. Finally, we open source the LSIC MATLAB
code under webpage https://sites.google.com/site/tuftsyuewu/source-code.Comment: 26 pages, 17 figures, and 7 table
StyleGAN Encoder-Based Attack for Block Scrambled Face Images
In this paper, we propose an attack method to block scrambled face images,
particularly Encryption-then-Compression (EtC) applied images by utilizing the
existing powerful StyleGAN encoder and decoder for the first time. Instead of
reconstructing identical images as plain ones from encrypted images, we focus
on recovering styles that can reveal identifiable information from the
encrypted images. The proposed method trains an encoder by using plain and
encrypted image pairs with a particular training strategy. While
state-of-the-art attack methods cannot recover any perceptual information from
EtC images, the proposed method discloses personally identifiable information
such as hair color, skin color, eyeglasses, gender, etc. Experiments were
carried out on the CelebA dataset, and results show that reconstructed images
have some perceptual similarities compared to plain images.Comment: To appear in APSIPA ASC 202
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