inside computer but does not work over the air • Over-the-air attack is not easy; never reported with RNN • Can AE be a realistic risk for speech recognition? • If it works, it can affect a large number of devices Audio AE playback broadcast Order 100 pizza! Order 100 pizza! Order 100 pizza!
attack on audio response systems (e.g., smartphone, smart speaker) • The world first audio AE that works with deep model over the air • Audio signal has higher diffuseness (e.g., speaker, broadcast) • Audio AE can affect a larger number of devices than image AE Original audio audio AE (recognized as “Hello World”) original audio adversarial noise audio AE deepspeech https://yumetaro.info/projects/audio-ae/
an abstract label to images • Given a model, can we extract photorealistic images recognized as a specific label? • If yes, it can be a risk (e.g., face authentication) “Keanu Reeves” model inversion? recognition
generator and discriminator • Given a random number, generator tries to generate currency that appears to be real • Given either of fake or real currency, discriminator tries to distinguish them correctly • Update generator and discriminator alternately until generator defeats discriminator Fake sample Generate fake Real sample Generator Distinguish fake and real Discriminator Random number
deep neural networks • generator: generate fake images look like “real” • discriminator: discriminate fake images and real images Generator discriminate fake from real Discriminator random number real face images fake face images
Application Security and Privacy (CODASPY18), to appear • What we usually do: • Disassemble samples and obtain instructions • Interpret instructions line-by-line • So time-consuming. • What we want: • Disassemble samples and obtain instructions • Find instructions characterizing the malware automatically Dis- assemble Analyze by human
be embedded into CNN • Find regions in the image that are expected to characterize the target class Examples of correspondences between the region in images and the underlined word in captions obtained from the feature map [Xu+, 2015]
IRC server and enter a chat room • A malware family that spread explosively around 2004 to construct a large botnet [Liang+, 2007] • execute commands sent over IRC • intercepts HTTP / FTP communication to steal login information of PAYPAL[Goel+, 2006]. An image of Backdoor.Win32.Agobot.on, a sample belonging to Worm:Win32/Gaobot, and its attention map obtained. A function to redirect packets to designated destinations to perform DDoS attacks A function to ascertain whether the contents of the intercepted HTTP communication include strings like “PAYPAL”