  Neural networks have been proven to be both highly effective within computer
vision, and highly vulnerable to adversarial attacks. Consequently, as the use
of neural networks increases due to their unrivaled performance, so too does
the threat posed by adversarial attacks. In this work, we build towards
addressing the challenge of adversarial robustness by exploring the
relationship between the mini-batch size used during adversarial sample
generation and the strength of the adversarial samples produced. We demonstrate
that an increase in mini-batch size results in a decrease in the efficacy of
the samples produced, and we draw connections between these observations and
the phenomenon of vanishing gradients. Next, we formulate loss functions such
that adversarial sample strength is not degraded by mini-batch size. Our
findings highlight a potential risk for underestimating the true (practical)
strength of adversarial attacks, and a risk of overestimating a model's
robustness. We share our codes to let others replicate our experiments and to
facilitate further exploration of the connections between batch size and
adversarial sample strength.
