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Found 20 papers, 7 papers with code
Randomized Smoothing under Attack: How Good is it in Pratice?
We first formally highlight the mismatch between a theoretical certification and the practice of attacks on classifiers.
Watermarking Images in Self-Supervised Latent Spaces
We revisit watermarking techniques based on pre-trained deep networks, in the light of self-supervised approaches.
Efficient Statistical Assessment of Neural Network Corruption Robustness
We quantify the robustness of a trained network to input uncertainties with a stochastic simulation inspired by the field of Statistical Reliability Engineering.
RoBIC: A benchmark suite for assessing classifiers robustness
Many defenses have emerged with the development of adversarial attacks.
SurFree: a fast surrogate-free black-box attack
This paper presents SurFree, a geometrical approach that achieves a similar drastic reduction in the amount of queries in the hardest setup: black box decision-based attacks (only the top-1 label is available).
Note: An alternative proof of the vulnerability of $k$-NN classifiers in high intrinsic dimensionality regions
This document proposes an alternative proof of the result contained in article "High intrinsic dimensionality facilitates adversarial attack: Theoretical evidence", Amsaleg et a..
Defending Adversarial Examples via DNN Bottleneck Reinforcement
This paper presents a DNN bottleneck reinforcement scheme to alleviate the vulnerability of Deep Neural Networks (DNN) against adversarial attacks.
Group Membership Verification with Privacy: Sparse or Dense?
Group membership verification checks if a biometric trait corresponds to one member of a group without revealing the identity of that member.
Joint Learning of Assignment and Representation for Biometric Group Membership
This paper proposes a framework for group membership protocols preventing the curious but honest server from reconstructing the enrolled biometric signatures and inferring the identity of querying clients.
Walking on the Edge: Fast, Low-Distortion Adversarial Examples
Adversarial examples of deep neural networks are receiving ever increasing attention because they help in understanding and reducing the sensitivity to their input.
Privacy Preserving Group Membership Verification and Identification
When convoking privacy, group membership verification checks if a biometric trait corresponds to one member of a group without revealing the identity of that member.
Hybrid Diffusion: Spectral-Temporal Graph Filtering for Manifold Ranking
State of the art image retrieval performance is achieved with CNN features and manifold ranking using a k-NN similarity graph that is pre-computed off-line.
Panorama to panorama matching for location recognition
Location recognition is commonly treated as visual instance retrieval on "street view" imagery.
Fast Spectral Ranking for Similarity Search
This makes the Euclidean nearest neighbor search biased for this task.
Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN Representations
The diffusion is carried out on descriptors of overlapping image regions rather than on a global image descriptor like in previous approaches.
Automatic discovery of discriminative parts as a quadratic assignment problem
Part-based image classification consists in representing categories by small sets of discriminative parts upon which a representation of the images is built.
Efficient Large-Scale Similarity Search Using Matrix Factorization
Experiments with standard image search benchmarks, including the Yahoo100M dataset comprising 100 million images, show that our method gives comparable (and sometimes superior) accuracy compared to exhaustive search while requiring only 10% of the vector operations and memory.
Memory vectors for similarity search in high-dimensional spaces
We study an indexing architecture to store and search in a database of high-dimensional vectors from the perspective of statistical signal processing and decision theory.
Orientation covariant aggregation of local descriptors with embeddings
Our geometric-aware aggregation strategy is effective for image search, as shown by experiments performed on standard benchmarks for image and particular object retrieval, namely Holidays and Oxford buildings.
