Dualattn-GAN: Text to Image Synthesis With Dual Attentional Generative Adversarial Network

Cai, Yali; Wang, Xiaoru; Yu, Zhihong; Fu, Li; Xu, Peirong; Li, Yueli; Li, Lixian

doi:10.1109/access.2019.2958864

Cited by 43 publications

(15 citation statements)

References 19 publications

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“…We have introduced several kinds of evasion attack strategies against DGMs, all of which are for computer vision. However, DGMs have wide applications in NLP, such as text to image (T2I) [93], [94], or text generation [95], [96], e.g., for writing poems [97] or medical record synthesis [98]. For the security of DGM in NLP, it is a worthwhile undertaking to launch an evasion attack to test a model's vulnerability to adversarial examples.…”

Section: Evasion Attacks On Nlpmentioning

confidence: 99%

Adversarial Attacks Against Deep Generative Models on Data: A Survey

Sun

Zhu

Zhang

et al. 2023

IEEE Trans. Knowl. Data Eng.

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Deep generative models have gained much attention given their ability to generate data for applications as varied as healthcare to financial technology to surveillance, and many more -the most popular models being generative adversarial networks (GANs) and variational auto-encoders (VAEs). Yet, as with all machine learning models, ever is the concern over security breaches and privacy leaks and deep generative models are no exception. In fact, these models have advanced so rapidly in recent years that work on their security is still in its infancy. In an attempt to audit the current and future threats against these models, and to provide a roadmap for defense preparations in the short term, we prepared this comprehensive and specialized survey on the security and privacy preservation of GANs and VAEs. Our focus is on the inner connection between attacks and model architectures and, more specifically, on five components of deep generative models: the training data, the latent code, the generators/decoders of GANs/VAEs, the discriminators/encoders of GANs/VAEs, and the generated data. For each model, component and attack, we review the current research progress and identify the key challenges. The paper concludes with a discussion of possible future attacks and research directions in the field.

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Section: Evasion Attacks On Nlpmentioning

confidence: 99%

Adversarial Attacks Against Deep Generative Models on Data: A Survey

Sun

Zhu

Zhang

et al. 2023

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

show abstract

“…Amodio et al [29] proposed TraVeLGAN to reduce the difficulty of CycleGAN training. To further improve the generation performance, the attention mechanism has been recently investigated in image translation, such as in [30].…”

Section: B Image-to-image Translationmentioning

confidence: 99%

Cross-View Image Translation Based on Local and Global Information Guidance

et al. 2021

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The cross-view image translation task is aimed at generating scene images from arbitrary views. However, due to the great differences in the shapes and contents of the various views, the quality of the generated images is degraded. Small objects, such as vehicles' shapes and details, are not clear, which causes them to be structurally inconsistent with the semantic map used to guide the generation process. To solve this problem, we propose a novel generative adversarial network based on a local and global information processing module (LAGGAN) to recover the image's details and structures. The network will further combine the input viewpoint image and the target semantic segmentation map to guide the generation of the target image from another viewpoint. The proposed LAGGAN includes a two-stage generator and a parameter-sharing discriminator. LAGGAN uses a new local and global information processing module (LAG) to generate highquality images from various views. Moreover, we integrate dilated convolutions into the discriminator to capture the global context, which can enhance the discriminative ability and further adjust the LAG module. Therefore, most semantic information can be preserved, and the details of the target viewpoint images can be translated more sharply. Quantitative and qualitative evaluation on both CVUSA and Dayton datasets attest to the fact that our method, LAGGAN, presents satisfactory perceptual results and is comparable to state-of-the-art methods on the cross-view image translation task.INDEX TERMS Aerial images, cross-view image translation, generative adversarial networks (GANs), ground-level images, local and global information processing module.

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“…In cGAN, the generation of samples can be conditioned on class information [40], text description [24], [41], audio [42], [43], skeleton [44], [45], and attributes [46]. Using an encoder-decoder architecture, the conditions can be applied to conduct domain changes on images such as image editing [47], image segmentation [48] and image inpainting [49].…”

Section: A Generative Adversarial Networkmentioning

confidence: 99%

Attribute-Aware Generative Design With Generative Adversarial Networks

Yuan

Moghaddam

2020

IEEE Access

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The designers' tendency to adhere to a specific mental set and heavy emotional investment in their initial ideas often limit their ability to innovate during the design ideation process. The shrinking timeto-market and the growing diversity of users' needs further exacerbate this gap. Recent advances in deep generative models have created new possibilities to overcome the cognitive obstacles of designers through automated generation or editing of design concepts. This paper explores the capabilities of generative adversarial networks (GAN) for automated, attribute-aware generative design of the visual attributes of a product. Specifically, a design attribute GAN (DA-GAN) model is developed for automated generation of fashion product images with the desired visual attributes. Experiments on a large fashion dataset signify the potentials of GAN for attribute-aware generative design, verify the ability of editing attributes with relatively higher accuracy and uncover several key challenges and research questions for future work.

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Dualattn-GAN: Text to Image Synthesis With Dual Attentional Generative Adversarial Network

Cited by 43 publications

References 19 publications

Adversarial Attacks Against Deep Generative Models on Data: A Survey

Adversarial Attacks Against Deep Generative Models on Data: A Survey

Cross-View Image Translation Based on Local and Global Information Guidance

Attribute-Aware Generative Design With Generative Adversarial Networks

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