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Found 18 papers, 1 papers with code
Semantic Adversarial Examples
This property is used by several defense methods to counter adversarial examples by applying denoising filters or training the model to be robust to small perturbations.
Assessing Shape Bias Property of Convolutional Neural Networks
In order to conduct large scale experiments, we propose using the model accuracy on images with reversed brightness as a metric to evaluate the shape bias property.
Attacking Automatic Video Analysis Algorithms: A Case Study of Google Cloud Video Intelligence API
At the end, we propose introducing randomness to video analysis algorithms as a countermeasure to our attacks.
On the Limitation of Convolutional Neural Networks in Recognizing Negative Images
To this end, we evaluate CNNs on negative images, since they share the same structure and semantics as regular images and humans can classify them correctly.
Google's Cloud Vision API Is Not Robust To Noise
For example, an adversary can bypass an image filtering system by adding noise to inappropriate images.
Deceiving Google's Cloud Video Intelligence API Built for Summarizing Videos
For this, we select an image, which is different from the video content, and insert it, periodically and at a very low rate, into the video.
Blocking Transferability of Adversarial Examples in Black-Box Learning Systems
Advances in Machine Learning (ML) have led to its adoption as an integral component in many applications, including banking, medical diagnosis, and driverless cars.
Deceiving Google's Perspective API Built for Detecting Toxic Comments
In this paper, we propose an attack on the Perspective toxic detection system based on the adversarial examples.
Image Block Loss Restoration Using Sparsity Pattern as Side Information
In this paper, we propose a method for image block loss restoration based on the notion of sparse representation.
Learning Temporal Dependence from Time-Series Data with Latent Variables
We consider the setting where a collection of time series, modeled as random processes, evolve in a causal manner, and one is interested in learning the graph governing the relationships of these processes.
Real-Time Impulse Noise Suppression from Images Using an Efficient Weighted-Average Filtering
In this paper, we propose a method for real-time high density impulse noise suppression from images.
Dropping Pixels for Adversarial Robustness
Deep neural networks are vulnerable against adversarial examples.
Are Odds Really Odd? Bypassing Statistical Detection of Adversarial Examples
In this paper, we first develop a classifier-based adaptation of the statistical test method and show that it improves the detection performance.
Federated Learning of User Authentication Models
In this paper, we propose Federated User Authentication (FedUA), a framework for privacy-preserving training of UA models.
Secure Federated Learning of User Verification Models
We consider the problem of training User Verification (UV) models in federated setup, where the conventional loss functions are not applicable due to the constraints that each user has access to the data of only one class and user embeddings cannot be shared with the server or other users.
Private Split Inference of Deep Networks
Splitting network computations between the edge device and the cloud server is a promising approach for enabling low edge-compute and private inference of neural networks.
Federated Learning of User Verification Models Without Sharing Embeddings
We consider the problem of training User Verification (UV) models in federated setting, where each user has access to the data of only one class and user embeddings cannot be shared with the server or other users.
Unsupervised Information Obfuscation for Split Inference of Neural Networks
In our method, the edge device runs the model up to a split layer determined based on its computational capacity.
