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# PabRod/encryptWithChaos.m Last active Jan 25, 2018

An easy version of the method for using chaos to encrypt an audio message described in Strogatz's Nonlinear dynamics and chaos, 1994
 %% Encrypt with chaos % An easy version of the method for using chaos to encrypt an audio message % described in Strogatz's Nonlinear dynamics and chaos, 1994 % % More information at: %% Load the original sound load handel; sampling_freq = Fs; signal = y'; length = size(signal, 2); %% Harvest chaos from the Lorenz equation % The Lorenz equation is the classical example of deterministic dynamical % system giving rise to chaos. The equation reads: % % $$\dot x = d \left( y - x \right)$$ % % $$\dot y = r x - y - e x z$$ % % $$\dot z = -bz + e x y$$ % % With the following set of parameters % % $$d = 10, r = 28, b = \frac{8}{3}, e = 1$$ % % we will find chaos for almost all initial conditions, being the only % exception the coordinate origin (an unstable steady state). % % So now we can pose the problem in Matlab code d = 10; r = 28; b = 8/3; e = 1; fun = @(t,x) [d*(x(2) - x(1)); r*x(1) - x(2) - e.*x(1).*x(3); -b*x(3) + e.*x(1).*x(2)]; %% Solve numerically % The Lorenz equation is not analytically solvable, so we will solve it % numerically for a given initial condition x0 = [0; 1; 0]; % Set initial condition tSpan = [0 300]; % Set time span sol = ode45(fun, tSpan, x0); % Create solver object % Resample with the desired time step t = linspace(tSpan(1), tSpan(end), length); % We want a vector with the same... x = deval(sol, t); % ... length as the signal %% Extract a one-dimensional time series % An easy way to extract a one-dimensional time series out of the Lorenz % attractor is focusing only on one coordinate. Z in our case timeSeriesX = x(1,:); timeSeriesZNormalized = timeSeriesX./max(abs(timeSeriesX)); %% Use it as encoding key strength = 5000; key = strength*timeSeriesZNormalized; coded_signal = signal + key; decoded_signal = coded_signal - key; %% Plot the signals figure; subplot(4,1,1); plot(t, signal, 'Color', 'b'); title('Signal'); subplot(4,1,2); plot(t, key, 'Color', 'r'); title('Key'); subplot(4,1,3); plot(t, coded_signal, 'Color', 'g'); title('Coded signal (Signal + Key)'); subplot(4,1,4); plot(t, decoded_signal, 'Color', 'b'); title('Recovered signal (Coded - Key)'); %% Export to audio files % % The _sonification_ process follows this steps: % % * Normalize the signal to the range [-1,1] % * Pick a sampling frequency % * Export as _.wav_ % % Normalization codedNormalized = coded_signal./max(abs(coded_signal)); decodedNormalized = decoded_signal./max(abs(decoded_signal)); % Exporting audiowrite('signal.wav', signal, sampling_freq); audiowrite('coded.wav', codedNormalized, sampling_freq); audiowrite('decoded.wav', decodedNormalized, sampling_freq); %% For direct playback use % % soundsc(decodedNormalized, sampling_freq); % %% Credits % Pablo Rodríguez-Sánchez % % % % April 2016