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Found 8 papers, 1 papers with code
Resurrecting Trust in Facial Recognition: Mitigating Backdoor Attacks in Face Recognition to Prevent Potential Privacy Breaches
Biometric data, such as face images, are often associated with sensitive information (e. g medical, financial, personal government records).
Robust Training Using Natural Transformation
We target attributes of the input images that are independent of the class identification, and manipulate those attributes to mimic real-world natural transformations (NaTra) of the inputs, which are then used to augment the training dataset of the image classifier.
OCTOPUS: Overcoming Performance andPrivatization Bottlenecks in Distributed Learning
We introduce a new distributed/collaborative learning scheme to address communication overhead via latent compression, leveraging global data while providing privatization of local data without additional cost due to encryption or perturbation.
Adversarial Defense by Latent Style Transformations
The intuition behind our approach is that the essential characteristics of a normal image are generally consistent with non-essential style transformations, e. g., slightly changing the facial expression of human portraits.
Defending Adversarial Attacks via Semantic Feature Manipulation
In this paper, we propose a one-off and attack-agnostic Feature Manipulation (FM)-Defense to detect and purify adversarial examples in an interpretable and efficient manner.
OIAD: One-for-all Image Anomaly Detection with Disentanglement Learning
Our key insight is that the impact of small perturbation on the latent representation can be bounded for normal samples while anomaly images are usually outside such bounded intervals, referred to as structure consistency.
Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models
In this paper, we demonstrate a backdoor threat to transfer learning tasks on both image and time-series data leveraging the knowledge of publicly accessible Teacher models, aimed at defeating three commonly-adopted defenses: \textit{pruning-based}, \textit{retraining-based} and \textit{input pre-processing-based defenses}.
Generating Semantic Adversarial Examples via Feature Manipulation
We further demonstrate the existence of a universal, image-agnostic semantic adversarial example.
