492 research outputs found
Robust Watermarking Using Inverse Gradient Attention
Watermarking is the procedure of encoding desired information into an image
to resist potential noises while ensuring the embedded image has little
perceptual perturbations from the original image. Recently, with the tremendous
successes gained by deep neural networks in various fields, digital
watermarking has attracted increasing number of attentions. The neglect of
considering the pixel importance within the cover image of deep neural models
will inevitably affect the model robustness for information hiding. Targeting
at the problem, in this paper, we propose a novel deep watermarking scheme with
Inverse Gradient Attention (IGA), combing the ideas of adversarial learning and
attention mechanism to endow different importance to different pixels. With the
proposed method, the model is able to spotlight pixels with more robustness for
embedding data. Besides, from an orthogonal point of view, in order to increase
the model embedding capacity, we propose a complementary message coding module.
Empirically, extensive experiments show that the proposed model outperforms the
state-of-the-art methods on two prevalent datasets under multiple settings.Comment: 9 pages, 6 figure
Design and Analysis of Fusion Algorithm for Multi-Frame Super-Resolution Image Reconstruction using Framelet
A enhanced fusion algorithm for generating a super resolution image from a sequence of low-resolution images captured from identical scene apparently a video, based on framelet have been designed and analyzed. In this paper an improved analytical method of image registration is used which integrates nearest neighbor method and gradient method. Comparing to Discrete Wavelet Transform (DWT) the Framelet Transform (FrT) have tight frame filter bank that offers symmetry and permits shift in invariance. Therefore using framelet this paper also present a framelet based enhanced fusion for choosing the fused framelet co-efficient that provides detailed edges and good spatial information with adequate de-noising. The proposed algorithm also has high advantage and computationally fast which are most needed for satellite imaging, medical imaging diagnosis, military surveillance, remote sensing etc.Defence Science Journal, Vol. 65, No. 4, July 2015, pp. 292-299, DOI: http://dx.doi.org/10.14429/dsj.65.826
Robust watermarking for magnetic resonance images with automatic region of interest detection
Medical image watermarking requires special considerations compared to ordinary watermarking methods. The first issue is the detection of an important area of the image called the Region of Interest (ROI) prior to starting the watermarking process. Most existing ROI detection procedures use manual-based methods, while in automated methods the robustness against intentional or unintentional attacks has not been considered extensively. The second issue is the robustness of the embedded watermark against different attacks. A common drawback of existing watermarking methods is their weakness against salt and pepper noise. The research carried out in this thesis addresses these issues of having automatic ROI detection for magnetic resonance images that are robust against attacks particularly the salt and pepper noise and designing a new watermarking method that can withstand high density salt and pepper noise. In the ROI detection part, combinations of several algorithms such as morphological reconstruction, adaptive thresholding and labelling are utilized. The noise-filtering algorithm and window size correction block are then introduced for further enhancement. The performance of the proposed ROI detection is evaluated by computing the Comparative Accuracy (CA). In the watermarking part, a combination of spatial method, channel coding and noise filtering schemes are used to increase the robustness against salt and pepper noise. The quality of watermarked image is evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and the accuracy of the extracted watermark is assessed in terms of Bit Error Rate (BER). Based on experiments, the CA under eight different attacks (speckle noise, average filter, median filter, Wiener filter, Gaussian filter, sharpening filter, motion, and salt and pepper noise) is between 97.8% and 100%. The CA under different densities of salt and pepper noise (10%-90%) is in the range of 75.13% to 98.99%. In the watermarking part, the performance of the proposed method under different densities of salt and pepper noise measured by total PSNR, ROI PSNR, total SSIM and ROI SSIM has improved in the ranges of 3.48-23.03 (dB), 3.5-23.05 (dB), 0-0.4620 and 0-0.5335 to 21.75-42.08 (dB), 20.55-40.83 (dB), 0.5775-0.8874 and 0.4104-0.9742 respectively. In addition, the BER is reduced to the range of 0.02% to 41.7%. To conclude, the proposed method has managed to significantly improve the performance of existing medical image watermarking methods
Deep Learning for Reversible Steganography: Principles and Insights
Deep-learning\textendash{centric} reversible steganography has emerged as a
promising research paradigm. A direct way of applying deep learning to
reversible steganography is to construct a pair of encoder and decoder, whose
parameters are trained jointly, thereby learning the steganographic system as a
whole. This end-to-end framework, however, falls short of the reversibility
requirement because it is difficult for this kind of monolithic system, as a
black box, to create or duplicate intricate reversible mechanisms. In response
to this issue, a recent approach is to carve up the steganographic system and
work on modules independently. In particular, neural networks are deployed in
an analytics module to learn the data distribution, while an established
mechanism is called upon to handle the remaining tasks. In this paper, we
investigate the modular framework and deploy deep neural networks in a
reversible steganographic scheme referred to as prediction-error modulation, in
which an analytics module serves the purpose of pixel intensity prediction. The
primary focus of this study is on deep-learning\textendash{based} context-aware
pixel intensity prediction. We address the unsolved issues reported in related
literature, including the impact of pixel initialisation on prediction accuracy
and the influence of uncertainty propagation in dual-layer embedding.
Furthermore, we establish a connection between context-aware pixel intensity
prediction and low-level computer vision and analyse the performance of several
advanced neural networks
DiffWA: Diffusion Models for Watermark Attack
With the rapid development of deep neural networks(DNNs), many robust blind
watermarking algorithms and frameworks have been proposed and achieved good
results. At present, the watermark attack algorithm can not compete with the
watermark addition algorithm. And many watermark attack algorithms only care
about interfering with the normal extraction of the watermark, and the
watermark attack will cause great visual loss to the image. To this end, we
propose DiffWA, a conditional diffusion model with distance guidance for
watermark attack, which can restore the image while removing the embedded
watermark. The core of our method is training an image-to-image conditional
diffusion model on unwatermarked images and guiding the conditional model using
a distance guidance when sampling so that the model will generate unwatermarked
images which is similar to original images. We conducted experiments on
CIFAR-10 using our proposed models. The results shows that the model can remove
the watermark with good effect and make the bit error rate of watermark
extraction higher than 0.4. At the same time, the attacked image will maintain
good visual effect with PSNR more than 31 and SSIM more than 0.97 compared with
the original image
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