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Found 7 papers, 3 papers with code
Breaking Temporal Consistency: Generating Video Universal Adversarial Perturbations Using Image Models
To address this challenge, we introduce the Breaking Temporal Consistency (BTC) method, which is the first attempt to incorporate temporal information into video attacks using image models.
Introducing Competition to Boost the Transferability of Targeted Adversarial Examples through Clean Feature Mixup
Deep neural networks are widely known to be susceptible to adversarial examples, which can cause incorrect predictions through subtle input modifications.
Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input
To tackle this limitation, we propose the object-based diverse input (ODI) method that draws an adversarial image on a 3D object and induces the rendered image to be classified as the target class.
Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization
It significantly outperforms traditional and deep neural network-based methods in detecting and localizing tampered regions.
Ranked #3 on
Image Manipulation Detection
on Casia V1+
DHNet: Double MPEG-4 Compression Detection via Multiple DCT Histograms
In this article, we aim to detect the double compression of MPEG-4, a universal video codec that is built into surveillance systems and shooting devices.
Frame-rate Up-conversion Detection Based on Convolutional Neural Network for Learning Spatiotemporal Features
The proposed network uses a stack of consecutive frames as the input and effectively learns interpolation artifacts using network blocks to learn spatiotemporal features.
Deep Convolutional Neural Network for Identifying Seam-Carving Forgery
Seam carving is a representative content-aware image retargeting approach to adjust the size of an image while preserving its visually prominent content.
