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- W2945185634 abstract "Deep Convolutional Neural Networks (DCNN) have proven to be an exceptional tool for object recognition in various computer vision applications. However, recent findings have shown that such state of the art models can be easily deceived by inserting slight imperceptible perturbations to key pixels in the input image. In this paper, we focus on deceiving Automatic Target Recognition(ATR) classiers. These classiers are built to recognize specified targets in a scene and also simultaneously identify their class types. In our work, we explore the vulnerabilities of DCNN-based target classifiers. We demonstrate significant progress in developing infrared adversarial target by adding small perturbations to the input image such that the image perturbation cannot be easily detected. The algorithm is built to adapt to both targeted and non-targeted adversarial attacks. Our findings reveal promising results that reflect serious implications of adversarial attacks." @default.
- W2945185634 created "2019-05-29" @default.
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- W2945185634 date "2019-05-14" @default.
- W2945185634 modified "2023-09-26" @default.
- W2945185634 title "Design of adversarial targets: fooling deep ATR systems" @default.
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- W2945185634 doi "https://doi.org/10.1117/12.2518945" @default.
- W2945185634 hasPublicationYear "2019" @default.
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