ADVERSARIAL HUJUM USULLARI YORDAMIDA OBYEKTLARNI ANIQLASh MODELLARINI ChALG‘ITISh VA ULARNING BARQARORLIGINI BAHOLASh

Avtorlar

Gilt sózler: FGSM, YOLOv5, PGD, Faster R-CNN, ResNet18, CNN

Annotaciya

Ushbu tadqiqotda konvolyutsion neyron tarmoqlarga asoslangan obyekt aniqlash tizimlari, xususan, YOLOv5 modeliga qarshi adversarial hujumlar samaradorligi o‘rganildi. Hujum usullari sifatida Fast Gradient Sign Method (FGSM) hamda uning takomillashtirilgan variantlari (Iterative FGSM va PGD) qo‘llanildi. ResNet18 modeli yordamida hosil qilingan adversarial perturbatsiyalar asosida original rasm va hujumga uchragan rasmning obyekt aniqlash natijalari solishtirildi. Natijalar shuni ko‘rsatdiki, hatto kichik miqdordagi shovqin ham obyekt aniqlash modelini sezilarli darajada chalg‘itishi mumkin. Adversarial hujumlar sun’iy intellekt modellarining, ayniqsa chuqur o‘rganishga asoslangan tasvirni tanish tizimlarining asosiy zaifliklaridan biri hisoblanadi. FGSM va PGD kabi hujumlar model kirishini kichik perturbatsiyalar orqali o‘zgartirib, modelni noto‘g‘ri klassifikatsiya qilishga majbur qiladi.

Paydalanılǵan ádebiyatlar

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