Pemanfaatan Deep Convolutional Auto-encoder untuk Mitigasi Serangan Adversarial Attack pada Citra Digital
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Abstrak
Serangan adversarial pada citra digital merupakan ancaman serius bagi penggunaan teknologi machine learning dalam berbagai aplikasi kehidupan sehari-hari. Teknik Fast Gradient Sign Method (FGSM) telah terbukti efektif dalam melakukan serangan pada model machine learning, termasuk pada citra digital yang terdapat dalam dataset ImageNet. Penelitian ini bertujuan untuk mengatasi permasalahan tersebut dengan memanfaatkan teknik Deep Convolutional Auto-encoder (AE) sebagai metode mitigasi serangan adversarial pada citra digital. Hasil penelitian menunjukkan bahwa serangan FGSM dapat dilakukan pada sebagian besar citra digital, namun ada beberapa citra yang lebih tahan terhadap serangan. Selain itu, teknik mitigasi AE efektif dalam mengurangi dampak dari serangan adversarial pada sebagian besar citra digital. Akurasi model serangan dan mitigasi masing-masing sebesar 14.58% dan 91.67%.
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