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Found 46 papers, 9 papers with code
An Exceptional Dataset For Rare Pancreatic Tumor Segmentation
Pancreatic NEuroendocrine Tumors (pNETs) are very rare endocrine neoplasms that account for less than 5% of all pancreatic malignancies, with an incidence of only 1-1. 5 cases per 100, 000.
Exploring Depth Information for Detecting Manipulated Face Videos
As one of the important face features, the face depth map, which has shown to be effective in other areas such as face recognition or face detection, is unfortunately paid little attention to in literature for face manipulation detection.
Federated Learning with Label-Masking Distillation
Inspired by this, we propose a label-masking distillation approach termed FedLMD to facilitate federated learning via perceiving the various label distributions of each client.
Privacy-Preserving Student Learning with Differentially Private Data-Free Distillation
The main idea is generating synthetic data to learn a student that can mimic the ability of a teacher well-trained on private data.
ScreenMark: Watermarking Arbitrary Visual Content on Screen
Visual Screen Content (VSC), is particularly susceptible to theft and leakage through screenshots, a vulnerability that current watermarking methods fail to adequately address. To address these challenges, we propose ScreenMark, a robust and practical watermarking method designed specifically for arbitrary VSC protection.
Are handcrafted filters helpful for attributing AI-generated images?
Recently, a vast number of image generation models have been proposed, which raises concerns regarding the misuse of these artificial intelligence (AI) techniques for generating fake images.
Cover-separable Fixed Neural Network Steganography via Deep Generative Models
Image steganography is the process of hiding secret data in a cover image by subtle perturbation.
Search, Examine and Early-Termination: Fake News Detection with Annotation-Free Evidences
Pioneer researches recognize evidences as crucial elements in fake news detection apart from patterns.
DANCE: Dual-View Distribution Alignment for Dataset Condensation
Specifically, from the inner-class view, we construct multiple "middle encoders" to perform pseudo long-term distribution alignment, making the condensed set a good proxy of the real one during the whole training process; while from the inter-class view, we use the expert models to perform distribution calibration, ensuring the synthetic data remains in the real class region during condensing.
Online Prompt Pricing based on Combinatorial Multi-Armed Bandit and Hierarchical Stackelberg Game
Unlike the existing fixed pricing mode, the PBT pricing mechanism we propose is more flexible and diverse, which is more in accord with the transaction needs of real-world scenarios.
What Makes Good Few-shot Examples for Vision-Language Models?
Despite the notable advancements achieved by leveraging pre-trained vision-language (VL) models through few-shot tuning for downstream tasks, our detailed empirical study highlights a significant dependence of few-shot learning outcomes on the careful selection of training examples - a facet that has been previously overlooked in research.
Regeneration Based Training-free Attribution of Fake Images Generated by Text-to-Image Generative Models
Comprehensive experiments reveal that (1) Our method can effectively attribute fake images to their source models, achieving comparable attribution performance with the state-of-the-art method; (2) Our method has high scalability ability, which is well adapted to real-world attribution scenarios.
Purified and Unified Steganographic Network
It is also shown to be capable of imperceptibly carrying the steganographic networks in a purified network.
Object-oriented backdoor attack against image captioning
After training with the poisoned data, the attacked model behaves normally on benign images, but for poisoned images, the model will generate some sentences irrelevant to the given image.
Fact-checking based fake news detection: a review
This paper reviews and summarizes the research results on fact-based fake news from the perspectives of tasks and problems, algorithm strategies, and datasets.
PROMPT-IML: Image Manipulation Localization with Pre-trained Foundation Models Through Prompt Tuning
We observe that humans tend to discern the authenticity of an image based on both semantic and high-frequency information, inspired by which, the proposed framework leverages rich semantic knowledge from pre-trained visual foundation models to assist IML.
From Covert Hiding to Visual Editing: Robust Generative Video Steganography
Traditional video steganography methods are based on modifying the covert space for embedding, whereas we propose an innovative approach that embeds secret message within semantic feature for steganography during the video editing process.
Model Copyright Protection in Buyer-seller Environment
The main idea of ISNN is to make a DNN sensitive to the key and copyright information.
Steal My Artworks for Fine-tuning? A Watermarking Framework for Detecting Art Theft Mimicry in Text-to-Image Models
In various fine-tune scenarios and against watermark attack methods, our research confirms that analyzing the distribution of watermarks in artificially generated images reliably detects unauthorized mimicry.
PatchCraft: Exploring Texture Patch for Efficient AI-generated Image Detection
We observe that the texture patches of images tend to reveal more traces left by generative models compared to the global semantic information of the images.
Securing Fixed Neural Network Steganography
To deal with this issue, we propose a key-based FNNS scheme to improve the security of the FNNS, where we generate key-controlled perturbations from the FNN for data embedding.
Physical Invisible Backdoor Based on Camera Imaging
Subsequently, we elaborate a special network architecture, which is easily compromised by our backdoor attack, by leveraging the attributes of the CFA interpolation algorithm and combining it with the feature extraction block in the camera identification model.
DRAW: Defending Camera-shooted RAW against Image Manipulation
RAW files are the initial measurement of scene radiance widely used in most cameras, and the ubiquitously-used RGB images are converted from RAW data through Image Signal Processing (ISP) pipelines.
RetouchingFFHQ: A Large-scale Dataset for Fine-grained Face Retouching Detection
However, the lack of large-scale and fine-grained face retouching datasets has been a major obstacle to progress in this field.
Towards Deep Network Steganography: From Networks to Networks
In this paper, we propose deep network steganography for the covert communication of DNN models.
StyleStegan: Leak-free Style Transfer Based on Feature Steganography
Our method consists of two main components: a style transfer method that accomplishes artistic stylization on the original image and an image steganography method that embeds content feature secrets on the stylized image.
Generative Steganographic Flow
In the forward mapping, secret data is hidden in the input latent of Glow model to generate stego images.
Generative Steganography Diffusion
However, existing GAN-based GS methods cannot completely recover the hidden secret data due to the lack of network invertibility, while Flow-based methods produce poor image quality due to the stringent reversibility restriction in each module.
Multi-modal Fake News Detection on Social Media via Multi-grained Information Fusion
The easy sharing of multimedia content on social media has caused a rapid dissemination of fake news, which threatens society's stability and security.
Steganography of Steganographic Networks
We propose to disguise a steganographic network (termed as the secret DNN model) into a stego DNN model which performs an ordinary machine learning task (termed as the stego task).
Exploring Depth Information for Face Manipulation Detection
As one of the important face features, the face depth map, which has shown to be effective in other areas such as the face recognition or face detection, is unfortunately paid little attention to in literature for detecting the manipulated face images.
Learning to Immunize Images for Tamper Localization and Self-Recovery
Image immunization (Imuge) is a technology of protecting the images by introducing trivial perturbation, so that the protected images are immune to the viruses in that the tampered contents can be auto-recovered.
Hiding Images in Deep Probabilistic Models
As an instantiation, we adopt a SinGAN, a pyramid of generative adversarial networks (GANs), to learn the patch distribution of one cover image.
Generative Steganography Network
A new steganographic approach called generative steganography (GS) has emerged recently, in which stego images (images containing secret data) are generated from secret data directly without cover media.
Image Generation Network for Covert Transmission in Online Social Network
Online social networks have stimulated communications over the Internet more than ever, making it possible for secret message transmission over such noisy channels.
Robust Watermarking for Video Forgery Detection with Improved Imperceptibility and Robustness
We jointly train a 3D-UNet-based watermark embedding network and a decoder that predicts the tampering mask.
Bootstrapping Multi-view Representations for Fake News Detection
Representations from each view are separately used to coarsely predict the fidelity of the whole news, and the multimodal representations are able to predict the cross-modal consistency.
Image Protection for Robust Cropping Localization and Recovery
Existing image cropping detection schemes ignore that recovering the cropped-out contents can unveil the purpose of the behaved cropping attack.
A DTCWT-SVD Based Video Watermarking resistant to frame rate conversion
To address this issue, we present a new video watermarking based on joint Dual-Tree Cosine Wavelet Transformation (DTCWT) and Singular Value Decomposition (SVD), which is resistant to frame rate conversion.
Multimodal Fake News Detection via CLIP-Guided Learning
The results indicate that the proposed framework has a better capability in mining crucial features for fake news detection.
Imperceptible Backdoor Attack: From Input Space to Feature Representation
A U-net-based network is employed to generate concrete parameters of multinomial distribution for each benign input.
Invertible Image Dataset Protection
We develop a reversible adversarial example generator (RAEG) that introduces slight changes to the images to fool traditional classification models.
From Image to Imuge: Immunized Image Generation
At the recipient's side, the verifying network localizes the malicious modifications, and the original content can be approximately recovered by the decoder, despite the presence of the attacks.
RWN: Robust Watermarking Network for Image Cropping Localization
At the recipient's side, ACP extracts the watermark from the attacked image, and we conduct feature matching on the original and extracted watermark to locate the position of the crop in the original image plane.
Hiding Images into Images with Real-world Robustness
The existing image embedding networks are basically vulnerable to malicious attacks such as JPEG compression and noise adding, not applicable for real-world copyright protection tasks.
Perceptual Robust Hashing for Color Images with Canonical Correlation Analysis
Local color features of significant corner points on outer boundaries of ring-ribbons are extracted through color vector angles (CVA), and color low-order moments (CLMs) is utilized to extract global color features.
