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+\begin{figure}[t]
+\centering
+\resizebox{.68\textwidth}{!}{%
+\begin{tikzpicture}
+
+    \node [rectangle,draw,thick,minimum width=1.5cm, minimum height=0.75cm] (targetmodel) {$\targetmodel$};
+    
+    \node[below of=targetmodel,yshift=-0.3cm,database,database radius=0.4cm,database segment height=0.2cm, label={below:\footnotesize $\traindata: (X, Y)$}] (trainingdata) {};
+
+    \node [left of=targetmodel,minimum width=0.75cm,xshift=-0.8cm,fill=red!20,rectangle,draw,thick,label={below:\footnotesize Input}] (inputrecord) {$X'(\omega)$};
+    \node [right of=targetmodel,xshift=1.5cm,rectangle,draw,thick] (outputpred) {$\targetmodel(X'(\omega))$};
+    % \node [below of=outputpred,minimum width=1.5cm,rectangle,draw,thick] (explanation) {$\phi(x)$};
+
+    \begin{scope}[on background layer]
+        \node (tm1) [fit=(targetmodel) (trainingdata), fill= gray!20, rounded corners, inner sep=0.1cm, label={above:\footnotesize }] {};
+    \end{scope}
+
+    \node [right of=outputpred,rectangle,draw,thick,minimum width=1.5cm, minimum height=0.75cm,xshift=1.2cm,fill= gray!20] (attmodel) {$\attackmodel$};
+
+    \node [right of=attmodel,xshift=0.6cm,minimum width=0.75cm,rectangle,draw,thick] (attout) {$S(\omega)$};
+
+    \node[below of=attmodel,database,database radius=0.4cm,database segment height=0.2cm,yshift=-0.3cm, label={below:\footnotesize $\auxdata: (X', Y', S')$}] (auxdata) {};
+
+\begin{scope}[on background layer]
+    \node (models) [fit=(attmodel) (outputpred) (attout) (auxdata), fill= red!20, rounded corners, inner sep=0.1cm] {};
+\end{scope}
+
+
+\draw[->,ultra thick] (inputrecord.east) -- node[anchor=south, align=center] {\em\footnotesize } (targetmodel.west);
+\draw[->,ultra thick] (targetmodel.east) -- node[anchor=south, align=center] {\em\footnotesize } (outputpred.west);
+% \draw[->,ultra thick] (targetmodel.east) -- node[anchor=south, align=center] {\em\footnotesize } (explanation.west);
+
+
+\draw[->,ultra thick, dashed] (outputpred.east) -- node[anchor=south, align=center] {\em\footnotesize } (attmodel.west);
+% \draw[->,ultra thick, dashed] (explanation.east) -- node[anchor=south, align=center] {\em\footnotesize } (attmodel.west);
+\draw[->,ultra thick] (attmodel.east) -- node[anchor=south, align=center] {\em\footnotesize } (attout.west);
+\draw[->,ultra thick,dashed] (trainingdata.north) -- node[anchor=south, align=center,label={[yshift=-0.2cm]right:\footnotesize Train}] {\em\footnotesize } (targetmodel.south);
+\draw[->,ultra thick,dashed] (auxdata.north) -- node[anchor=south, align=center,label={[yshift=-0.2cm]right:\footnotesize Train}] {\em\footnotesize } (attmodel.south);
+
+
+\end{tikzpicture}
+}
+\vspace{-1mm}
+\caption{\adv wants to infer sensitive attributes for an input given its prediction. \adv trains $\attackmodel$ on $\auxdata$ to map $\targetmodel(X'(\omega))$ to $S'(\omega)$. Once trained, \adv only uses $\targetmodel$'s outputs as input to $\attackmodel$ to infer sensitive attributes. \colorbox{red!20}{red} indicates accessible by \adv.} 
+\vspace{-1mm}
+\label{fig:tm2}
+\end{figure}
+\ No newline at end of file