Boosting Adversarial Attacks on Neural Networks with Better Optimizer

Convolutional neural networks have outperformed humans in image recognition tasks, but they remain vulnerable to attacks from adversarial examples. Since these data are crafted by adding imperceptible noise to normal images, their existence poses potential security threats to deep learning systems. Sophisticated adversarial examples with strong attack performance can also be used as a tool to evaluate the robustness of a model. However, the success rate of adversarial attacks can be further improved in black-box environments. Therefore, this study combines a modified Adam gradient descent algorithm with the iterative gradient-based attack method. The proposed Adam Iterative Fast Gradient Method is then used to improve the transferability of adversarial examples. Extensive experiments on ImageNet showed that the proposed method offers a higher attack success rate than existing iterative methods. By extending our method, we achieved a state-of-the-art attack success rate of 95.0% on defense models

Additive value of 3T cardiovascular magnetic resonance coronary angiography for detecting coronary artery disease

Abstract Background The purpose of the work was to evaluate the incremental diagnostic value of free-breathing, contrast-enhanced, whole-heart, 3 T cardiovascular magnetic resonance coronary angiography (CE-MRCA) to stress/rest myocardial perfusion imaging (MPI) and late gadolinium enhancement (LGE) imaging for detecting coronary artery disease (CAD). Methods Fifty-one patients with suspected CAD underwent a comprehensive cardiovascular magnetic resonance (CMR) examination (CE-MRCA, MPI, and LGE). The additive diagnostic value of MRCA to MPI and LGE was evaluated using invasive x-ray coronary angiography (XA) as the standard for defining functionally significant CAD (≥ 50% stenosis in vessels > 2 mm in diameter). Results 90.2% (46/51) patients (54.0 ± 11.5 years; 71.7% men) completed CE-MRCA successfully. On per-patient basis, compared to MPI/LGE alone or MPI alone, the addition of MRCA resulted in higher sensitivity (100% vs. 76.5%, p < 0.01), no change in specificity (58.3% vs. 66.7%, p = 0.6), and higher accuracy (89.1% vs 73.9%, p < 0.01) for CAD detection (prevalence = 73.9%). Compared to LGE alone, the addition of CE-MRCA resulted in higher sensitivity (97.1% vs. 41.2%, p < 0.01), inferior specificity (83.3% vs. 91.7%, p = 0.02), and higher diagnostic accuracy (93.5% vs. 54.3%, p < 0.01). Conclusion The inclusion of successful free-breathing, whole-heart, 3 T CE-MRCA significantly improved the sensitivity and diagnostic accuracy as compared to MPI and LGE alone for CAD detection