Targeted Adversarial Abundance Attacks for Hyperspectral Image Classification

Untargeted adversarial abundance attacks in hyper-spectral image classification have received growing attention due to their budget efficiency and physical plausibility, which arising from adherence to spectral mixing principles. However, it is difficult to design abundance-domain perturbation for targeted attacks to simultaneously suppress evidence for the source class while amplifying evidence for a specified target. This paper proposes a targeted adversarial abundance attack framework that steers reconstructed spectra toward the decision region of the designated class. The proposed method first applies deep learning based unmixing to obtain endmembers and abundances without reliance on external spectral libraries. A class-guided perturbation strategy is proposed to enforce abundance-vector similarity and feature-space alignment. Then a gradient conflict resolution mechanism is proposed to balance multiple loss objectives, which stabilizes updates and maintains small, physically consistent perturbations. Experiments on benchmark hyperspectral image datasets demonstrate that the proposed targeted adversarial abundance-attack framework achieves targeted success with improved imperceptibility and small perturbation norms.