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Found 16 papers, 7 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.
Forgery-aware Adaptive Vision Transformer for Face Forgery Detection
To circumvent these issues, we propose a novel Forgery-aware Adaptive Vision Transformer (FA-ViT).
Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery Detection
Face forgery detection is essential in combating malicious digital face attacks.
ReLoc: A Restoration-Assisted Framework for Robust Image Tampering Localization
The ReLoc framework mainly consists of an image restoration module and a tampering localization module.
Forensicability Assessment of Questioned Images in Recapturing Detection
We integrate the trained FANet with practical recapturing detection schemes in face anti-spoofing and recaptured document detection tasks.
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.
Robust Privacy-Preserving Motion Detection and Object Tracking in Encrypted Streaming Video
In this paper, we propose an efficient and robust privacy-preserving motion detection and multiple object tracking scheme for encrypted surveillance video bitstreams.
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.
Detection of Deepfake Videos Using Long Distance Attention
And a spatial-temporal model is proposed which has two components for capturing spatial and temporal forgery traces in global perspective respectively.
Improving Cost Learning for JPEG Steganography by Exploiting JPEG Domain Knowledge
To address the issue, in this paper we extend an existing automatic cost learning scheme to JPEG, where the proposed scheme called JEC-RL (JPEG Embedding Cost with Reinforcement Learning) is explicitly designed to tailor the JPEG DCT structure.
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.
An Automatic Cost Learning Framework for Image Steganography Using Deep Reinforcement Learning
In SPAR-RL, an agent utilizes a policy network which decomposes the embedding process into pixel-wise actions and aims at maximizing the total rewards from a simulated steganalytic environment, while the environment employs an environment network for pixel-wise reward assignment.
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.
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