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Found 16 papers, 6 papers with code
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.
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.
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.
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.
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.
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.
Fingerprinting Generative Adversarial Networks
In this paper, we present the first fingerprinting scheme for the Intellectual Property (IP) protection of GANs.
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.
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.
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).
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.
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-OTA: Unveiling Backdoor Threats in Self-Supervised Learning for Object Detection
The extensive adoption of Self-supervised learning (SSL) has led to an increased security threat from backdoor attacks.
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.
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, whereas these methods also pose potential and severe security threats to humanity.
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.
