Analysis of Adversarial Attacks on Skin Cancer Recognition

Huq, Aminul; Pervin, Mst. Tasnim

doi:10.1109/icodsa50139.2020.9212850

Cited by 10 publications

(8 citation statements)

References 18 publications

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“…In clinical applications, it has been discovered that noises that are difficult for humans to detect frequently cause significant interference to the diagnostic model, limiting the utility of deep learning in the actual world. To improve the model’s robustness ( 97 ), performed adversarial training on MobileNet and VGG-16 using the innovative attacking models FGSM and PGD for skin cancer classification. Firstly, two white-box attacks based on Projected Gradient Descent (PGD) and Fast Gradient Sign Method (FGSM) were used to test the robustness of these models.…”

Section: Methods For Typical and Frontier Problems In Skin Cancer Cla...mentioning

confidence: 99%

Skin Cancer Classification With Deep Learning: A Systematic Review

Chen

Zeng

et al. 2022

Front. Oncol.

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Skin cancer is one of the most dangerous diseases in the world. Correctly classifying skin lesions at an early stage could aid clinical decision-making by providing an accurate disease diagnosis, potentially increasing the chances of cure before cancer spreads. However, achieving automatic skin cancer classification is difficult because the majority of skin disease images used for training are imbalanced and in short supply; meanwhile, the model’s cross-domain adaptability and robustness are also critical challenges. Recently, many deep learning-based methods have been widely used in skin cancer classification to solve the above issues and achieve satisfactory results. Nonetheless, reviews that include the abovementioned frontier problems in skin cancer classification are still scarce. Therefore, in this article, we provide a comprehensive overview of the latest deep learning-based algorithms for skin cancer classification. We begin with an overview of three types of dermatological images, followed by a list of publicly available datasets relating to skin cancers. After that, we review the successful applications of typical convolutional neural networks for skin cancer classification. As a highlight of this paper, we next summarize several frontier problems, including data imbalance, data limitation, domain adaptation, model robustness, and model efficiency, followed by corresponding solutions in the skin cancer classification task. Finally, by summarizing different deep learning-based methods to solve the frontier challenges in skin cancer classification, we can conclude that the general development direction of these approaches is structured, lightweight, and multimodal. Besides, for readers’ convenience, we have summarized our findings in figures and tables. Considering the growing popularity of deep learning, there are still many issues to overcome as well as chances to pursue in the future.

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Section: Methods For Typical and Frontier Problems In Skin Cancer Cla...mentioning

confidence: 99%

Skin Cancer Classification With Deep Learning: A Systematic Review

Chen

Zeng

et al. 2022

Front. Oncol.

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show abstract

“…FGSM reduced the model’s accuracy by 36% while One-pixel by only 2–3%. Huq and Pervin [ 18 ] applied the FGSM and PGD attacks on dermoscopic images for skin cancer recognition. The model’s performance decreased by up to 75%.…”

Section: Related Workmentioning

confidence: 99%

Digital Watermarking as an Adversarial Attack on Medical Image Analysis with Deep Learning

Apostolidis

Papakostas

2022

J. Imaging

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In the past years, Deep Neural Networks (DNNs) have become popular in many disciplines such as Computer Vision (CV), and the evolution of hardware has helped researchers to develop many powerful Deep Learning (DL) models to deal with several problems. One of the most important challenges in the CV area is Medical Image Analysis. However, adversarial attacks have proven to be an important threat to vision systems by significantly reducing the performance of the models. This paper brings to light a different side of digital watermarking, as a potential black-box adversarial attack. In this context, apart from proposing a new category of adversarial attacks named watermarking attacks, we highlighted a significant problem, as the massive use of watermarks, for security reasons, seems to pose significant risks to vision systems. For this purpose, a moment-based local image watermarking method is implemented on three modalities, Magnetic Resonance Images (MRI), Computed Tomography (CT-scans), and X-ray images. The introduced methodology was tested on three state-of-the art CV models, DenseNet 201, DenseNet169, and MobileNetV2. The results revealed that the proposed attack achieved over 50% degradation of the model’s performance in terms of accuracy. Additionally, MobileNetV2 was the most vulnerable model and the modality with the biggest reduction was CT-scans.

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“…In addition, they used two different architectures, a DNN and a hybrid DNN model that is combined with segmentation techniques and they showed that the hybrid model is much more robust than a conventional DNN on adversarial attacks. Huq et al [72] analyzed adversarial attacks for skin cancer recognition. They experimented with VGG16 and MobileNet on the HAM10000 dataset, trying to classify an image into seven categories.…”

Section: Existing Adversarial Attacks On Medical Imagesmentioning

confidence: 99%

A Survey on Adversarial Deep Learning Robustness in Medical Image Analysis

Apostolidis

Papakostas

2021

Electronics

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In the past years, deep neural networks (DNN) have become popular in many disciplines such as computer vision (CV), natural language processing (NLP), etc. The evolution of hardware has helped researchers to develop many powerful Deep Learning (DL) models to face numerous challenging problems. One of the most important challenges in the CV area is Medical Image Analysis in which DL models process medical images—such as magnetic resonance imaging (MRI), X-ray, computed tomography (CT), etc.—using convolutional neural networks (CNN) for diagnosis or detection of several diseases. The proper function of these models can significantly upgrade the health systems. However, recent studies have shown that CNN models are vulnerable under adversarial attacks with imperceptible perturbations. In this paper, we summarize existing methods for adversarial attacks, detections and defenses on medical imaging. Finally, we show that many attacks, which are undetectable by the human eye, can degrade the performance of the models, significantly. Nevertheless, some effective defense and attack detection methods keep the models safe to an extent. We end with a discussion on the current state-of-the-art and future challenges.

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Analysis of Adversarial Attacks on Skin Cancer Recognition

Cited by 10 publications

References 18 publications

Skin Cancer Classification With Deep Learning: A Systematic Review

Skin Cancer Classification With Deep Learning: A Systematic Review

Digital Watermarking as an Adversarial Attack on Medical Image Analysis with Deep Learning

A Survey on Adversarial Deep Learning Robustness in Medical Image Analysis

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