Subodh Sharma

Tooba Khan, Kumar Madhukar, Subodh Vishnu Sharma

The generation of adversarial inputs has become a crucial issue in establishing the robustness and trustworthiness of deep neural nets, especially when they are used in safety-critical application domains such as autonomous vehicles and precision medicine. However, the problem poses multiple practical challenges, including scalability issues owing to large-sized networks, and the generation of adversarial inputs that lack important qualities such as naturalness and output-impartiality. This problem shares its end goal with the task of patching neural nets where small changes in some of the network's weights need to be discovered so that upon applying these changes, the modified net produces the desirable output for a given set of inputs. We exploit this connection by proposing to obtain an adversarial input from a patch, with the underlying observation that the effect of changing the weights can also be brought about by changing the inputs instead. Thus, this paper presents a novel way to generate input perturbations that are adversarial for a given network by using an efficient network patching technique. We note that the proposed method is significantly more effective than the prior state-of-the-art techniques.

Chinmay Narayan, Subodh Sharma, Shibashis Guha et al.

Nondeterminism in scheduling is the cardinal reason for difficulty in proving correctness of concurrent programs. A powerful proof strategy was recently proposed [6] to show the correctness of such programs. The approach captured data-flow dependencies among the instructions of an interleaved and error-free execution of threads. These data-flow dependencies were represented by an inductive data-flow graph (iDFG), which, in a nutshell, denotes a set of executions of the concurrent program that gave rise to the discovered data-flow dependencies. The iDFGs were further transformed in to alternative finite automatons (AFAs) in order to utilize efficient automata-theoretic tools to solve the problem. In this paper, we give a novel and efficient algorithm to directly construct AFAs that capture the data-flow dependencies in a concurrent program execution. We implemented the algorithm in a tool called ProofTraPar to prove the correctness of finite state cyclic programs under the sequentially consistent memory model. Our results are encouranging and compare favorably to existing state-of-the-art tools.