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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.

Unchecked discharges of untreated water from combined sewer systems pose critical hazards to public health and ecosystems. To address this, regional sanitation authorities like the Metropolitan Sewer District of Greater Cincinnati (MSDGC) have begun collecting massive streams of hydrological telemetry, looking toward deep learning (DL) methodologies—specifically recurrent neural networks (RNN)—to forecast these spill events. Crucially, determining whether a DL model is truly suitable for this domain requires a structured analysis of the environment in which it operates. This paper introduces a requirements engineering methodology grounded in problem frames to map out and organize stakeholder concerns. This approach systematically evaluates real-world conditions where standard assumptions of high-quality data break down, translating these insights into actionable software testing benchmarks known as metamorphic relations, which govern both data input adjustments and output validations. Deploying this methodology within MSDGC’s operational context provides a rigorous, standardized baseline to assess how effectively competing RNN designs fulfill core operational mandates.