39 citations · 73 across the 14 of their papers we have counts for
14 papers
Probabilistic Verification of Voice Anti-Spoofing Models
Evgeny Kushnir, Alexandr Kozodaev, Dmitrii Korzh +3
Recent advances in generative models have amplified the risk of malicious misuse of speech synthesis technologies, enabling adversaries to impersonate target speakers and access se…
Contract And Conquer: How to Provably Compute Adversarial Examples for a Black-Box Model?
Anna Chistyakova, Mikhail Pautov
Black-box adversarial attacks are widely used as tools to test the robustness of deep neural networks against malicious perturbations of input data aimed at a specific change in th…
RandMark: On Random Watermarking of Visual Foundation Models
Anna Chistyakova, Mikhail Pautov
Being trained on large and diverse datasets, visual foundation models (VFMs) can be fine-tuned to achieve remarkable performance and efficiency in various downstream computer visio…
Towards Robust Speech Deepfake Detection via Human-Inspired Reasoning
Artem Dvirniak, Evgeny Kushnir, Dmitrii Tarasov +5
The modern generative audio models can be used by an adversary in an unlawful manner, specifically, to impersonate other people to gain access to private information. To mitigate t…
ActiveMark: on watermarking of visual foundation models via massive activations
Anna Chistyakova, Mikhail Pautov
Being trained on large and vast datasets, visual foundation models (VFMs) can be fine-tuned for diverse downstream tasks, achieving remarkable performance and efficiency in various…
Spread them Apart: Towards Robust Watermarking of Generated Content
Mikhail Pautov, Danil Ivanov, Andrey V. Galichin +2
Generative models that can produce realistic images have improved significantly in recent years. The quality of the generated content has increased drastically, so sometimes it is…
GLiRA: Black-Box Membership Inference Attack via Knowledge Distillation
Andrey V. Galichin, Mikhail Pautov, Alexey Zhavoronkin +2
While Deep Neural Networks (DNNs) have demonstrated remarkable performance in tasks related to perception and control, there are still several unresolved concerns regarding the pri…
Certification of Speaker Recognition Models to Additive Perturbations
Dmitrii Korzh, Elvir Karimov, Mikhail Pautov +2
Speaker recognition technology is applied to various tasks, from personal virtual assistants to secure access systems. However, the robustness of these systems against adversarial…
Probabilistically Robust Watermarking of Neural Networks
Mikhail Pautov, Nikita Bogdanov, Stanislav Pyatkin +2
As deep learning (DL) models are widely and effectively used in Machine Learning as a Service (MLaaS) platforms, there is a rapidly growing interest in DL watermarking techniques t…
General Lipschitz: Certified Robustness Against Resolvable Semantic Transformations via Transformation-Dependent Randomized Smoothing
Dmitrii Korzh, Mikhail Pautov, Olga Tsymboi +1
Randomized smoothing is the state-of-the-art approach to construct image classifiers that are provably robust against additive adversarial perturbations of bounded magnitude. Howev…
Translate your gibberish: black-box adversarial attack on machine translation systems
Andrei Chertkov, Olga Tsymboi, Mikhail Pautov +1
Neural networks are deployed widely in natural language processing tasks on the industrial scale, and perhaps the most often they are used as compounds of automatic machine transla…
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…
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…