Search Results for author:
Found 9 papers, 5 papers with code
Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4
As a significant representation of dynamic malware behavior, the API (Application Programming Interface) sequence, comprised of consecutive API calls, has progressively become the dominant feature of dynamic analysis methods.
Evading Detection Actively: Toward Anti-Forensics against Forgery Localization
In adversarial training, SEAR employs a forgery localization model as a supervisor to explore tampering features and constructs a deep-learning concealer to erase corresponding traces.
STD-NET: Search of Image Steganalytic Deep-learning Architecture via Hierarchical Tensor Decomposition
We propose a normalized distortion threshold to evaluate the sensitivity of each involved convolutional layer of the base model to guide STD-NET to compress target network in an efficient and unsupervised approach, and obtain two network structures of different shapes with low computation cost and similar performance compared with the original one.
Universal Deep Network for Steganalysis of Color Image based on Channel Representation
The proposed method includes preprocessing, convolutional, and classification modules.
Self-Adversarial Training incorporating Forgery Attention for Image Forgery Localization
In this paper, we propose a self-adversarial training strategy and a reliable coarse-to-fine network that utilizes a self-attention mechanism to localize forged regions in forgery images.
MCTSteg: A Monte Carlo Tree Search-based Reinforcement Learning Framework for Universal Non-additive Steganography
Recent research has shown that non-additive image steganographic frameworks effectively improve security performance through adjusting distortion distribution.
Image Steganography based on Iteratively Adversarial Samples of A Synchronized-directions Sub-image
In this paper, we present a novel steganography scheme denoted as ITE-SYN (based on ITEratively adversarial perturbations onto a SYNchronized-directions sub-image), by which security data is embedded with synchronizing modification directions to enhance security and then iteratively increased perturbations are added onto a sub-image to reduce loss with cover class label of the target CNN classifier.
Detection of Deep Network Generated Images Using Disparities in Color Components
In this paper, we address the problem of detecting deep network generated (DNG) images by analyzing the disparities in color components between real scene images and DNG images.
Multimedia
A multi-branch convolutional neural network for detecting double JPEG compression
In this paper, we present a CNN solution by using raw DCT (discrete cosine transformation) coefficients from JPEG images as input.
