An Empirical Study on GAN-Based Traffic Congestion Attack Analysis: A Visualized Method

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545 490 590 449 616C412 641 363 650 290 650H42V622C120 615 128 612 128 527V125C128 40 120 34 38 28V0H300V28C221 34 212 40 212 125V284H244C295 284 312 272 329 244C359 195 395 133 430 84C475 19 516 -3 592 -7C603 -8 615 -8 627 -8L631 18ZM212 316V563C212 591 215 602 223 607C231 613 248 617 277 617C352 617 423 577 423 469C423 415 407 375 368 345C343 324 310 316 260 316H212Z"></path></g></svg> trends under different epochs</td></tr><tr class="fig-image" id="b"><td><object data="https://static.hindawi.com/articles/wcmc/volume-2020/8823300/figures/8823300.fig.007b.svgz" name="8823300.fig.007b" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(b) </b><svg height="12.0627pt" id="M219" style="vertical-align:-3.309361pt" version="1.1" viewbox="-0.0498162 -8.75334 44.4454 12.0627" width="44.4454pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M861 0V28C774 35 771 41 768 147L759 509C756 612 762 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<div>Training epoch influence on CycleGAN and pix2pix.</div>

Wireless Communications and Mobile Computing

fig7

Figure 7

Figure 7: An Empirical Study on GAN-Based Traffic Congestion Attack Analysis: A Visualized Method 