Robust Data Augmentation Generative Adversarial Network for Object Detection

Lee, Hyungtak; Kang, Seongju; Chung, Kwangsue

doi:10.3390/s23010157

Cited by 12 publications

(9 citation statements)

References 26 publications

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“…GANs are a powerful deep generative model trained with an adversarial procedure. GANs have undergone several modifications since they were first proposed to solve several different problems in different domains, e.g., physics [ 18 ], healthcare [ 19 ], or object detection [ 20 ]. To analyze the state-of-the-art in what concerns GANs used for synthetic data generation, as well as synthetic data generation methods, we reviewed recently published scientific papers [ 21 , 22 , 23 ].…”

Section: Related Workmentioning

confidence: 99%

Evaluating Synthetic Medical Images Using Artificial Intelligence with the GAN Algorithm

Abdusalomov

Nasimov

Nasimova

et al. 2023

Sensors

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In recent years, considerable work has been conducted on the development of synthetic medical images, but there are no satisfactory methods for evaluating their medical suitability. Existing methods mainly evaluate the quality of noise in the images, and the similarity of the images to the real images used to generate them. For this purpose, they use feature maps of images extracted in different ways or distribution of images set. Then, the proximity of synthetic images to the real set is evaluated using different distance metrics. However, it is not possible to determine whether only one synthetic image was generated repeatedly, or whether the synthetic set exactly repeats the training set. In addition, most evolution metrics take a lot of time to calculate. Taking these issues into account, we have proposed a method that can quantitatively and qualitatively evaluate synthetic images. This method is a combination of two methods, namely, FMD and CNN-based evaluation methods. The estimation methods were compared with the FID method, and it was found that the FMD method has a great advantage in terms of speed, while the CNN method has the ability to estimate more accurately. To evaluate the reliability of the methods, a dataset of different real images was checked.

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Section: Related Workmentioning

confidence: 99%

Evaluating Synthetic Medical Images Using Artificial Intelligence with the GAN Algorithm

Abdusalomov

Nasimov

Nasimova

et al. 2023

Sensors

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“…presented a patch attack to fool the automated driving systems by modifying the road signs under various conditions. Furthermore, many researchers have evaluated this attack in various applications, such as object detection and FR in digital 36 and physical environments 37 , 38 …”

Section: Literature Surveymentioning

confidence: 99%

M-SAN: a patch-based transferable adversarial attack using the multi-stack adversarial network

Agrawal

Bhatnagar

2023

J. Electron. Imag.

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In recent times, deep neural networks (DNN) have been extensively used in several areas and have achieved great success. State-of-the-art face recognition (FR) systems have gained high accuracy using DNN. However, researchers have found that DNN-based systems fail when facing an adversarial attack on images. In adversarial attacks, the adversary modifies the face images in a manner that the human does not perceive the changes in the generated image but FR systems are unable to recognize the faces correctly. We proposed a method to generate an adversarial attack. A multistack adversarial network (M-SAN) patch-based attack is generated using the generative adversarial network under the black-box settings. The M-SAN attack targets the features of the face images using a patch to fool the FR model as targeted and untargeted attacks. In the past, several attack generation methods have been presented under white-box settings. However, white-box settings need a model architecture and information about its parameters. Due to this, a single white-box attack is not able to fool different FR models. We propose an attack generation approach that is based on black-box settings in which an attacker does not have access to the target model parameters. The attack is generated with the help of the surrogate model and then transferred to the various target models. The proposed M-SAN attack is applied to FR models including FaceNet, ArcFace, and CosFace on the labeled face in the wild dataset.

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“…If a person wears the adversarial patch, they can disappear from the detector. Lee et al 31 . generated a special adversarial patch by improving the work of DPatch 32 .…”

Section: Related Workmentioning

confidence: 99%

Location-independent adversarial patch generation for object detection

Ding,

Sun,

Mao

et al. 2023

J. Electron. Imag.

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Object detection models are at the core of various computer vision tasks and have shown excellent performance on public datasets, but they also inherit the disadvantage of neural networks that they are vulnerable to adversarial example attacks. Adversarial patches are specific forms of adversarial examples that, as shown in previous studies, can only make specific objects (such as pedestrians and traffic signs), but not all objects, disappear. In addition, a patch must be placed on every object to deceive the detector. To solve the above problems, we propose a locationindependent adversarial patch generation method that can attack objects in the range to be detected with a single patch. By attacking the confidence loss of the object detector, we creatively assign a greater weight to the foreground region, which makes its confidence decrease faster and effectively guides the convergence direction of the adversarial patch in the training process. Furthermore, we glue the patches randomly on the images to make them less sensitive to location during patch training. Experimental results indicate that the patches generated using our proposed method are not restricted to specific areas of the image and provide a minimum recall of 29.5%.

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Robust Data Augmentation Generative Adversarial Network for Object Detection

Cited by 12 publications

References 26 publications

Evaluating Synthetic Medical Images Using Artificial Intelligence with the GAN Algorithm

Evaluating Synthetic Medical Images Using Artificial Intelligence with the GAN Algorithm

M-SAN: a patch-based transferable adversarial attack using the multi-stack adversarial network

Location-independent adversarial patch generation for object detection

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