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Found 8 papers, 4 papers with code
SemanticAdv: Generating Adversarial Examples via Attribute-conditioned Image Editing
Deep neural networks (DNNs) have achieved great successes in various vision applications due to their strong expressive power.
StyleFaceV: Face Video Generation via Decomposing and Recomposing Pretrained StyleGAN3
Notably, StyleFaceV is capable of generating realistic $1024\times1024$ face videos even without high-resolution training videos.
Text2Human: Text-Driven Controllable Human Image Generation
In this work, we present a text-driven controllable framework, Text2Human, for a high-quality and diverse human generation.
Few-shot Forgery Detection via Guided Adversarial Interpolation
Realistic visual media synthesis is becoming a critical societal issue with the surge of face manipulation models; new forgery approaches emerge at an unprecedented pace.
Can Shape Structure Features Improve Model Robustness Under Diverse Adversarial Settings?
Specifically, EdgeNetRob and EdgeGANRob first explicitly extract shape structure features from a given image via an edge detection algorithm.
SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editing
In this paper, we aim to explore the impact of semantic manipulation on DNNs predictions by manipulating the semantic attributes of images and generate "unrestricted adversarial examples".
Two-phase Hair Image Synthesis by Self-Enhancing Generative Model
Generating plausible hair image given limited guidance, such as sparse sketches or low-resolution image, has been made possible with the rise of Generative Adversarial Networks (GANs).
Precise Temporal Action Localization by Evolving Temporal Proposals
The performances of existing action localization approaches remain unsatisfactory in precisely determining the beginning and the end of an action.
