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Found 10 papers, 3 papers with code
Defending Against Universal Attacks Through Selective Feature Regeneration
Deep neural network (DNN) predictions have been shown to be vulnerable to carefully crafted adversarial perturbations.
Synthesized Texture Quality Assessment via Multi-scale Spatial and Statistical Texture Attributes of Image and Gradient Magnitude Coefficients
Performance evaluations on two synthesized texture databases demonstrate that our proposed RR synthesized texture quality metric significantly outperforms both full-reference and RR state-of-the-art quality metrics in predicting the perceived visual quality of the synthesized textures.
Full Reference Objective Quality Assessment for Reconstructed Background Images
To compare the performance of the proposed quality index with existing image quality assessment measures, we construct two different datasets consisting of reconstructed background images and corresponding subjective scores.
Generative Sensing: Transforming Unreliable Sensor Data for Reliable Recognition
The proposed generative sensing framework aims at transforming low-end, low-quality sensor data into higher quality sensor data in terms of achieved classification accuracy.
Can the early human visual system compete with Deep Neural Networks?
We study and compare the human visual system and state-of-the-art deep neural networks on classification of distorted images.
A Study and Comparison of Human and Deep Learning Recognition Performance Under Visual Distortions
In this work, we compare the performance of DNNs with human subjects on distorted images.
DeepCorrect: Correcting DNN models against Image Distortions
In this paper, we evaluate the effect of image distortions like Gaussian blur and additive noise on the activations of pre-trained convolutional filters.
Quality Resilient Deep Neural Networks
The "experts" in our model are trained on a particular type of distortion.
Visual Saliency Prediction Using a Mixture of Deep Neural Networks
The final saliency map is computed as a weighted mixture of the expert networks' output, with weights determined by a separate gating network.
Understanding How Image Quality Affects Deep Neural Networks
We show that the existing networks are susceptible to these quality distortions, particularly to blur and noise.
