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Found 18 papers, 7 papers with code
Towards Reliable Verification of Unauthorized Data Usage in Personalized Text-to-Image Diffusion Models
In this paper, we introduce SIREN, a novel methodology to proactively trace unauthorized data usage in black-box personalized text-to-image diffusion models.
Perception-guided Jailbreak against Text-to-Image Models
Specifically, we propose identifying a safe phrase that is similar in human perception yet inconsistent in text semantics with the target unsafe word and using it as a substitution.
Text Modality Oriented Image Feature Extraction for Detecting Diffusion-based DeepFake
The widespread use of diffusion methods enables the creation of highly realistic images on demand, thereby posing significant risks to the integrity and safety of online information and highlighting the necessity of DeepFake detection.
Let Real Images be as a Judger, Spotting Fake Images Synthesized with Generative Models
Unfortunately, the artifact patterns in fake images synthesized by different generative models are inconsistent, leading to the failure of previous research that relied on spotting subtle differences between real and fake.
Lips Are Lying: Spotting the Temporal Inconsistency between Audio and Visual in Lip-Syncing DeepFakes
In recent years, DeepFake technology has achieved unprecedented success in high-quality video synthesis, but these methods also pose potential and severe security threats to humanity.
Enhancing RAW-to-sRGB with Decoupled Style Structure in Fourier Domain
However, current methods often ignore the difference between cell phone RAW images and DSLR camera RGB images, a difference that goes beyond the color matrix and extends to spatial structure due to resolution variations.
GhostEncoder: Stealthy Backdoor Attacks with Dynamic Triggers to Pre-trained Encoders in Self-supervised Learning
Pre-trained image encoders can serve as feature extractors, facilitating the construction of downstream classifiers for various tasks.
SSL-Auth: An Authentication Framework by Fragile Watermarking for Pre-trained Encoders in Self-supervised Learning
Self-supervised learning (SSL), a paradigm harnessing unlabeled datasets to train robust encoders, has recently witnessed substantial success.
Hard Adversarial Example Mining for Improving Robust Fairness
Adversarial training (AT) is widely considered the state-of-the-art technique for improving the robustness of deep neural networks (DNNs) against adversarial examples (AE).
What can Discriminator do? Towards Box-free Ownership Verification of Generative Adversarial Network
In recent decades, Generative Adversarial Network (GAN) and its variants have achieved unprecedented success in image synthesis.
What can Discriminator do? Towards Box-free Ownership Verification of Generative Adversarial Networks
In recent decades, Generative Adversarial Network (GAN) and its variants have achieved unprecedented success in image synthesis.
Fingerprinting Image-to-Image Generative Adversarial Networks
Since the production of a commercial GAN requires substantial computational and human resources, the copyright protection of GANs is urgently needed.
Countering Malicious DeepFakes: Survey, Battleground, and Horizon
To fill this gap, in this paper, we provide a comprehensive overview and detailed analysis of the research work on the topic of DeepFake generation, DeepFake detection as well as evasion of DeepFake detection, with more than 318 research papers carefully surveyed.
FakePolisher: Making DeepFakes More Detection-Evasive by Shallow Reconstruction
At this moment, GAN-based image generation methods are still imperfect, whose upsampling design has limitations in leaving some certain artifact patterns in the synthesized image.
Amora: Black-box Adversarial Morphing Attack
In this paper, we investigate and introduce a new type of adversarial attack to evade FR systems by manipulating facial content, called \textbf{\underline{a}dversarial \underline{mor}phing \underline{a}ttack} (a. k. a.
FakeSpotter: A Simple yet Robust Baseline for Spotting AI-Synthesized Fake Faces
In recent years, generative adversarial networks (GANs) and its variants have achieved unprecedented success in image synthesis.
Towards a Robust Deep Neural Network in Texts: A Survey
Recently, studies have revealed adversarial examples in the text domain, which could effectively evade various DNN-based text analyzers and further bring the threats of the proliferation of disinformation.
Prediction of the Yield of Enzymatic Synthesis of Betulinic Acid Ester Using Artificial Neural Networks and Support Vector Machine
To simplify the number of times of optimization in experimental works, here, we use artificial neural network (ANN) and support vector machine (SVM) models for the prediction of yields of 3\b{eta}-O-phthalic ester of betulinic acid synthesized by betulinic acid and phthalic anhydride using lipase as biocatalyst.
