5 accepted papers
Conventional backdoor attacks on deep neural networks (DNNs) typically assume that an attacker can manipulate the training data or process. However, recent research introduces a more practical threat model by injecting backdoors at the inference stage. These approaches leverage bit flip attacks to m…
Applying deep learning to malware detection has drawn great attention due to its notable performance. With the increasing prevalence of cyberattacks targeting IoT devices, there is a parallel rise in the development of malware across various Instruction Set Architectures (ISAs). It is thus important
Binary code analysis is indispensable for a variety of software security tasks. Applying deep learning to binary code analysis has drawn great attention because of its notable performance. Today, source code is frequently compiled for various Instruction Set Architectures (ISAs). It is thus critical…
Noise feature modules are commonly used in image manipulation localization. However, different noise learning modules can only target limited tampering methods. In actual image tampering localization tasks, the tampering methods are unknown; it is difficult for a single noise feature module to match…
Existing image manipulation detection and localization methods tend to detect the trail of manipulation and achieve decent performance. So far, however, there has been little discussion about the category imbalance. In this paper, we propose a parallel attention mechanism based network to localize t…