288 citations · 556 across the 21 of their papers we have counts for
21 papers
Real-World Transferable Adversarial Attack on Face-Recognition Systems
Andrey Kaznacheev, Matvey Mikhalchuk, Andrey Kuznetsov +2
Adversarial attacks on face recognition (FR) systems pose a significant security threat, yet most are confined to the digital domain or require white-box access. We introduce GaP (…
Inverting Black-Box Face Recognition Systems via Zero-Order Optimization in Eigenface Space
Anton Razzhigaev, Matvey Mikhalchuk, Klim Kireev +3
Reconstructing facial images from black-box recognition models poses a significant privacy threat. While many methods require access to embeddings, we address the more challenging…
SafeAuto: Knowledge-Enhanced Safe Autonomous Driving with Multimodal Foundation Models
Jiawei Zhang, Xuan Yang, Taiqi Wang +3
Traditional autonomous driving systems often struggle to connect high-level reasoning with low-level control, leading to suboptimal and sometimes unsafe behaviors. Recent advances…
CIMRL: Combining IMitation and Reinforcement Learning for Safe Autonomous Driving
Jonathan Booher, Khashayar Rohanimanesh, Junhong Xu +5
Modern approaches to autonomous driving rely heavily on learned components trained with large amounts of human driving data via imitation learning. However, these methods require l…
Uniformly Safe RL with Objective Suppression for Multi-Constraint Safety-Critical Applications
Zihan Zhou, Jonathan Booher, Khashayar Rohanimanesh +3
Safe reinforcement learning tasks are a challenging domain despite being very common in the real world. The widely adopted CMDP model constrains the risks in expectation, which mak…
Nonparametric Uncertainty Quantification for Single Deterministic Neural Network
Nikita Kotelevskii, Aleksandr Artemenkov, Kirill Fedyanin +6
This paper proposes a fast and scalable method for uncertainty quantification of machine learning models' predictions. First, we show the principled way to measure the uncertainty…
Smoothed Embeddings for Certified Few-Shot Learning
Mikhail Pautov, Olesya Kuznetsova, Nurislam Tursynbek +2
Randomized smoothing is considered to be the state-of-the-art provable defense against adversarial perturbations. However, it heavily exploits the fact that classifiers map input o…
Many Heads but One Brain: Fusion Brain -- a Competition and a Single Multimodal Multitask Architecture
Daria Bakshandaeva, Denis Dimitrov, Vladimir Arkhipkin +8
Supporting the current trend in the AI community, we present the AI Journey 2021 Challenge called Fusion Brain, the first competition which is targeted to make the universal archit…
CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks
Mikhail Pautov, Nurislam Tursynbek, Marina Munkhoeva +3
In safety-critical machine learning applications, it is crucial to defend models against adversarial attacks -- small modifications of the input that change the predictions. Beside…
Manifold Hypothesis in Data Analysis: Double Geometrically-Probabilistic Approach to Manifold Dimension Estimation
Alexander Ivanov, Gleb Nosovskiy, Alexey Chekunov +7
Manifold hypothesis states that data points in high-dimensional space actually lie in close vicinity of a manifold of much lower dimension. In many cases this hypothesis was empiri…
Darker than Black-Box: Face Reconstruction from Similarity Queries
Anton Razzhigaev, Klim Kireev, Igor Udovichenko +1
Several methods for inversion of face recognition models were recently presented, attempting to reconstruct a face from deep templates. Although some of these approaches work in a…
Certified Robustness via Randomized Smoothing over Multiplicative Parameters of Input Transformations
Nikita Muravev, Aleksandr Petiushko
Currently the most popular method of providing robustness certificates is randomized smoothing where an input is smoothed via some probability distribution. We propose a novel appr…
MDMMT: Multidomain Multimodal Transformer for Video Retrieval
Maksim Dzabraev, Maksim Kalashnikov, Stepan Komkov +1
We present a new state-of-the-art on the text to video retrieval task on MSRVTT and LSMDC benchmarks where our model outperforms all previous solutions by a large margin. Moreover,…
Quadric Hypersurface Intersection for Manifold Learning in Feature Space
Fedor Pavutnitskiy, Sergei O. Ivanov, Evgeny Abramov +5
The knowledge that data lies close to a particular submanifold of the ambient Euclidean space may be useful in a number of ways. For instance, one may want to automatically mark an…
Robustness Threats of Differential Privacy
Nurislam Tursynbek, Aleksandr Petiushko, Ivan Oseledets
Differential privacy (DP) is a gold-standard concept of measuring and guaranteeing privacy in data analysis. It is well-known that the cost of adding DP to deep learning model is i…
Mutual Modality Learning for Video Action Classification
Stepan Komkov, Maksim Dzabraev, Aleksandr Petiushko
The construction of models for video action classification progresses rapidly. However, the performance of those models can still be easily improved by ensembling with the same mod…
Black-Box Face Recovery from Identity Features
Anton Razzhigaev, Klim Kireev, Edgar Kaziakhmedov +2
In this work, we present a novel algorithm based on an it-erative sampling of random Gaussian blobs for black-box face recovery, given only an output feature vector of deep face re…
Geometry-Inspired Top-k Adversarial Perturbations
Nurislam Tursynbek, Aleksandr Petiushko, Ivan Oseledets
The brittleness of deep image classifiers to small adversarial input perturbations has been extensively studied in the last several years. However, the main objective of existing p…
On adversarial patches: real-world attack on ArcFace-100 face recognition system
Mikhail Pautov, Grigorii Melnikov, Edgar Kaziakhmedov +2
Recent works showed the vulnerability of image classifiers to adversarial attacks in the digital domain. However, the majority of attacks involve adding small perturbation to an im…
Real-world adversarial attack on MTCNN face detection system
Edgar Kaziakhmedov, Klim Kireev, Grigorii Melnikov +2
Recent studies proved that deep learning approaches achieve remarkable results on face detection task. On the other hand, the advances gave rise to a new problem associated with th…
AdvHat: Real-world adversarial attack on ArcFace Face ID system
Stepan Komkov, Aleksandr Petiushko
In this paper we propose a novel easily reproducible technique to attack the best public Face ID system ArcFace in different shooting conditions. To create an attack, we print the…