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\begin{table}[!htb]
%\vspace{-3mm}
\caption{\adaptiveAIAHard and \adaptiveAIASoft outperform their respective baselines. We report attack accuracy over ten runs.}
\begin{center}
%\footnotesize
\scriptsize
\begin{tabular}{ l | c | c }
\hline
\rowcolor{LightCyan} & \multicolumn{2}{c}{\ref{tm:hard}}\\
\rowcolor{LightCyan} \textbf{Dataset} & \textbf{Baseline ($\upsilon$=0.50)} & \textbf{\adaptiveAIAHard}\\
\rowcolor{LightCyan} & \textbf{\race} | \textbf{\sex}& \textbf{\race} | \textbf{\sex}\\
\textbf{\census} & 0.50 $\pm$ 0.00 | 0.50 $\pm$ 0.00& \textbf{0.56 $\pm$ 0.01} | \textbf{0.58 $\pm$ 0.01} \\
\textbf{\compas}& \textbf{0.62 $\pm$ 0.03} | 0.50 $\pm$ 0.00& \textbf{0.62 $\pm$ 0.03} | \textbf{0.57 $\pm$ 0.03} \\
\textbf{\meps} & 0.51 $\pm$ 0.01 | \textbf{0.55 $\pm$ 0.02} & \textbf{0.53 $\pm$ 0.01} | \textbf{0.55 $\pm$ 0.01} \\
\textbf{\lfw} & 0.59 $\pm$ 0.00 | 0.64 $\pm$ 0.15& \textbf{0.61 $\pm$ 0.11} | \textbf{0.78 $\pm$ 0.05} \\
\hline
\rowcolor{LightCyan} & \multicolumn{2}{c}{\ref{tm:soft}} \\
\rowcolor{LightCyan} \textbf{Dataset} & \textbf{Baseline ($\upsilon$=0.50)} & \textbf{\adaptiveAIASoft} \\
\rowcolor{LightCyan} & \textbf{\race} | \textbf{\sex} & \textbf{\race} | \textbf{\sex}\\
\hline
\textbf{\census}& 0.50 $\pm$ 0.02 | 0.56 $\pm$ 0.04 & \textbf{0.61 $\pm$ 0.02} | \textbf{0.68 $\pm$ 0.01} \\
\textbf{\compas}& \textbf{0.62 $\pm$ 0.03} | 0.50 $\pm$ 0.00 & \textbf{0.62 $\pm$ 0.03} | \textbf{0.57 $\pm$ 0.03} \\
\textbf{\meps} & 0.52 $\pm$ 0.02 | 0.55 $\pm$ 0.02 & \textbf{0.60 $\pm$ 0.02} | \textbf{0.62 $\pm$ 0.02}\\
\textbf{\lfw} & 0.50 $\pm$ 0.10 | \textbf{0.77 $\pm$ 0.07} & \textbf{0.61 $\pm$ 0.10} | \textbf{0.79 $\pm$ 0.05}\\
\hline
\end{tabular}
\end{center}
\label{tab:global_threshold_withoutsattr}
%\vspace{-5mm}
\end{table}
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