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Graph neural networks (GNNs) have been successfully deployed across numerous practical domains, yet the precise mechanisms underlying their operations remain largely opaque. In response to this lack of clarity and to encourage the deployment of reliable AI systems, a variety of GNN explanation tools have been developed. Nonetheless, current interpretation techniques frequently encounter severe bottlenecks, including: (1) explanations that are restricted to localized individual instances; (2) poor generalizability when applied to unfamiliar graph structures; (3) the risk of creating structurally invalid graphs; and (4) confinement to specific task domains (such as only node or only graph classification). To resolve these deficiencies, we introduce a novel interpretation framework, GAN-GNNExplainer, which integrates a generator to construct explanations alongside a discriminator to supervise the production stage, thereby enhancing output dependability. Although advantageous, GAN-GNNExplainer still exhibits limitations in producing highly faithful explanations and shows suboptimal performance when tested on empirical datasets. To rectify these weaknesses, we present ACGAN-GNNExplainer, an advanced iteration that enhances GAN-GNNExplainer by utilizing a more robust discriminator to continuously monitor the generation phase, which yields explanatory outputs that are both dependable and faithful. Comprehensive empirical evaluations conducted on both artificial and empirical graph datasets confirm that our proposed methodologies outperform conventional GNN explanation frameworks.

Recent investigations have demonstrated that deep neural networks are susceptible to subtly modified input data. This paper introduces a novel untargeted white-box adversarial attack termed the dynamic programming-based sub-pixel score method (SPSM) attack (DPSPSM). This technique modifies conventional gradient-based white-box adversarial methods, which are typically restricted by a predetermined Hamming distance, by incorporating a dynamic programming framework. The method is driven by a pixel score metric approach, the SPSM, which is developed herein. Unlike traditional gradient-based adversarial attacks that introduce nearly imperceptible modifications across the entire image, DPSPSM operates rapidly and maintains robustness by altering only a restricted number of input pixels. The introduced algorithm quantizes the gradient updates using a score computed for every pixel, which aggregates the contributions across all channels. Experimental outcomes indicate that the DPSPSM successfully misleads the model, achieving a success rate of 30.45% on the CIFAR-10 test set and 29.30% on the CIFAR-100 test set.

A multitude of studies demonstrate that robust predictive models for identifying microorganisms can be successfully developed by combining Raman micro-spectroscopy with chemometric techniques, frequently yielding highly accurate outcomes. Progress in machine learning alongside increased software availability has simplified the process for researchers to construct predictive frameworks from intricate data arrays. Conversely, the underlying reasons for the high accuracy of these classifications remain under-investigated. This study analyzes Raman spectroscopic datasets from fungal spores and carotenoid-producing microorganisms to demonstrate that successful differentiation is frequently independent of peak positions or subtle variations in spectral band ratios caused by minor biochemical differences among the organisms. Instead, distinctive baseline variations in biochemically related microorganisms, which may be amplified by specific data preprocessing routines, play a critical role; furthermore, seemingly neutral spectral intervals can significantly influence a convolutional neural network. By applying a technique known as Gradient-weighted Class Activation Mapping, we investigate the internal mechanics of convolutional neural networks within microbiological domains to determine the precise Raman spectral bands driving successful classification.