Side-channel analysis is an important concern for the security of cryptographic implementations, and may lead to powerful key recovery attacks if no countermeasures are deployed. Therefore, various types of protection mechanisms have been proposed over ...

A machine learning model is private if it doesn't reveal (too much) about its training data. This three-part talk examines to what extent current networks are private. Standard models are not private. We develop an attack that extracts rare training ...

How can we gauge the privacy provided by machine learning algorithms? Models trained with differential privacy (DP) provably limit information leakage, but the question remains open for non-DP models. In this talk, we present multiple techniques for ...

As deepfakes become harder to detect by humans, more reliable detection methods are required to fight the spread of fake images and videos. In our work, we focus on PRNU-based detection methods, which, while popular in the image forensics scene, have ...

Fake speech detection aims to distinguish fake speech from natural speech. This paper presents an effective fake speech detection scheme based on residual network with transformer encoder (TE-ResNet) for improving the performance of fake speech ...

Our paper presents a systematic approach to investigate whether and how events can be identified and extracted during the use of video conferencing software. Our approach is based on the encrypted meta and multimedia data exchanged during video ...

Organizations publish and share more and more electronic documents like PDF files. Unfortunately, most organizations are unaware that these documents can compromise sensitive information like authors names, details on the information system and ...

Double compressed (DC) adaptive multi-rate (AMR) audio detection is an important but challenging audio forensic task which has received great attention over the last decade. Although the majority of the existing studies extract hand-crafted features and ...

Federated learning is a secure machine learning technology proposed to protect data privacy and security in machine learning model training. However, recent studies show that federated learning is vulnerable to backdoor attacks, such as model ...

Binarized Neural Networks (BNN) provide efficient implementations of Convolutional Neural Networks (CNN). This makes them particularly suitable to perform fast and memory-light inference of neural networks running on resource-constrained devices. ...

Deep Neural Networks (DNNs) are at the heart of many of today's most innovative technologies. With companies investing lots of resources to design, build and optimize these networks for their custom products, DNNs are now integral to many companies' ...

Boundary samples are special inputs to artificial neural networks crafted to identify the execution environment used for inference by the resulting output label. The paper presents and evaluates algorithms to generate transparent boundary samples. ...

Security and privacy is of great interest in biometric systems which can be offered by Match-on-Card (MoC) technology, successfully applied in several areas of biometrics. In finger vein recognition such a system is not available yet. Utilizing minutiae ...

Generation of synthetic biometric samples such as, for instance, fingerprint images gains more and more importance especially in view of recent cross-border regulations on security of private data. The reason is that biometric data is designated in ...

The progress in steganography is hampered by a gap between non-additive distortion functions, which capture well complex dependencies in natural images, and their additive counterparts, which are efficient for data embedding. This paper proposes a ...

In this paper, we present an Information Hiding approach that would be suitable for exfiltrating sensible information of Industrial Control Systems (ICS) by leveraging the long-term storage of process data in historian databases. We show how hidden ...

In this work, we revisit Perturbed Quantization steganography with modern tools available to the steganographer today, including near-optimal ternary coding and content-adaptive embedding with side-information. In PQ, side-information in the form of ...

Heterogeneous parallel steganography (HPS) has become a new trend of current streaming media voice steganography, which hides secret information in the frames of streaming media with multiple kinds of orthogonal steganography. Because of the complexity ...

In this paper, we investigate the effect of pretraining CNNs on ImageNet on their performance when refined for steganalysis of digital images. In many cases, it seems that just 'seeing' a large number of images helps with the convergence of the network ...

In this paper, we study the EfficientNet family pre-trained on ImageNet when used for steganalysis using transfer learning. We show that certain "surgical modifications" aimed at maintaining the input resolution in EfficientNet architectures ...

In this paper, we propose a novel DNN watermarking method that utilizes a learnable image transformation method with a secret key. The proposed method embeds a watermark pattern in a model by using learnable transformed images and allows us to remotely ...

For the protection of trained deep neural network(DNN) models, embedding watermarks into the weights of the DNN model have been considered. However, the amount of change in the weights is large in the conventional methods, and it is reported that the ...

Machine Learning is a well established tool used in a variety of applications. As training advanced models requires considerable amounts of meaningful data in addition to specific knowledge, a new business model separate models creators from model ...

Watermarking algorithms have been introduced in the past years to protect deep learning models against unauthorized re-distribution. We investigate the robustness and reliability of state-of-the-art deep neural network watermarking schemes. We focus on ...

The demand for methods to protect the Intellectual Property Rights (IPR) associated to Deep Neural Networks (DNNs) is rising. Watermarking has been recently proposed as a way to protect the IPR of DNNs and track their usages. Although a number of ...