3 research outputs found
Semiblind Image Deconvolution with Spatially Adaptive Total Variation Regularization
A semiblind image deconvolution algorithm with spatially adaptive total variation (SATV) regularization is introduced. The spatial information in different image regions is incorporated into regularization by using the edge indicator called difference eigenvalue to distinguish flat areas from edges. Meanwhile, the split Bregman method is used to optimize the proposed SATV model. The proposed algorithm integrates the spatial constraint and parametric blur-kernel and thus effectively reduces the noise in flat regions and preserves the edge information. Comparative results on simulated images and real passive millimeter-wave (PMMW) images are reported
Determining Sequence of Image Processing Technique (IPT) to Detect Adversarial Attacks
Developing secure machine learning models from adversarial examples is
challenging as various methods are continually being developed to generate
adversarial attacks. In this work, we propose an evolutionary approach to
automatically determine Image Processing Techniques Sequence (IPTS) for
detecting malicious inputs. Accordingly, we first used a diverse set of attack
methods including adaptive attack methods (on our defense) to generate
adversarial samples from the clean dataset. A detection framework based on a
genetic algorithm (GA) is developed to find the optimal IPTS, where the
optimality is estimated by different fitness measures such as Euclidean
distance, entropy loss, average histogram, local binary pattern and loss
functions. The "image difference" between the original and processed images is
used to extract the features, which are then fed to a classification scheme in
order to determine whether the input sample is adversarial or clean. This paper
described our methodology and performed experiments using multiple data-sets
tested with several adversarial attacks. For each attack-type and dataset, it
generates unique IPTS. A set of IPTS selected dynamically in testing time which
works as a filter for the adversarial attack. Our empirical experiments
exhibited promising results indicating the approach can efficiently be used as
processing for any AI model
Development of Some Novel Spatial-Domain and Transform-Domain Digital Image Filters
Some spatial-domain and transform-domain digital image filtering algorithms have been developed in this thesis to suppress additive white Gaussian noise (AWGN). In many occasions, noise in digital images is found to be additive in nature with uniform power in the whole bandwidth and with Gaussian probability distribution. Such a noise is referred to as Additive White Gaussian Noise (AWGN). It is difficult to suppress AWGN since it corrupts almost all pixels in an image. The arithmetic mean filter, commonly known as Mean filter, can be employed to suppress AWGN but it introduces a blurring effect. Image denoising is usually required to be performed before display or further processing like segmentation, feature extraction, object recognition, texture analysis, etc. The purpose of denoising is to suppress the noise quite efficiently while retaining the edges and other detailed features as much as possible