Hello everyone
I have been working in the field of adversarial robustness for a few months now. I have been studying many literatures on adversarial robustness, and here I got a few questions that feel like I have not satisfactorily been answered:
1. Are we able to properly frame adversarial robustness?
2. It feels to me like the actual reality (take for eg., a traffic scenario) is very high-dimensional. If, in reality, the actual reality is truly high-dimensional, then the images captured for a high-dimensional space are low-dimensional. Now if this feeling is true then might it be that while we are converting the high-dimensional space to a low-dimensional representation we are losing critical information that is responsible for causing adversarial issues in DL models?
3. Why are we not trying to address adversarial robustness from a cognitive approach? It feels like the nature or the human brain are adversarially robust system. If it is so, then I think we need to investigate whether artificial models trained by principles of cognitive science are more or less robust than normal DNNs.
Sometimes it looks like everything in this universe has a fundamental geometric configuration. Adversarial attacks damage the outer configuration due to which the models misclassify, but the fundamental geometric configuration or the fundamental manifold structure is not hampered by adversarial attacks.
Are we fundamentally lacking something?
Dear Subhodeep,
Thank you for posing such a pertinent and profound question regarding adversarial robustness. My research explores complementary perspectives on robustness in complex systems, applied to the human brain and consciousness, which might offer a distinct angle to this discussion.
On dimensionality and information loss I agree that high-dimensional reduction can compromise robustness. From my model of the Emergent Cartography of Consciousness, "negative information" that fragments the system can analogously lead to "decoherence" states, where functional integration is lost. This suggests that the way critical information is managed is key to system persistence.
My work postulates that consciousness emerges from the dynamic interaction of pillars such as memory, emotion, thought, perception, and corporeality. The true robustness of biological systems, like the brain, may lie in their capacity for self-organization and functional reorganization in the face of extreme perturbations. The concept of Isostasis in my research precisely describes this vital persistence in complex human systems beyond structural and adaptive collapse, through chaotic functional reorganization. This is a qualitatively different form of robustness than typically trained in DNNs.
I share the intuition about the relevance of underlying geometry. My approach is based on neurotopology, studying how global brain activation structures and the morphodynamics of its networks facilitate the emergence of consciousness and its coherence. Adversarial attacks (or biological/cognitive perturbations) could be impacting the functional topological form of the network, and robustness would imply the system's ability to preserve or re-establish these essential forms.
This is a fascinating field for transdisciplinary dialogue. I am developing these concepts in my thesis ("Emergent Cartography of Consciousness: Proposal for a Neurotopological Psychometrics for the Measurement of Human Coherence") and recent essays.
I hope my comment was helpful, and if you have any questions, don't hesitate to let me know. I will soon upload my work to English to make it easier to read.
Regards,
Arturo Salazar Chon
Student, Universidad Autónoma de Occidente
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