1084 lines
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HTML
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38 KiB
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2021-01-08
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1.4k 字
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5 分钟
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眼前一亮的工作！海豚音攻击，试着复现看看（贫穷版）
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</div>
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<h1 id="海豚音攻击-复现"><a href="#海豚音攻击-复现" class="headerlink" title="海豚音攻击-复现"></a>海豚音攻击-复现</h1><p>文章中提到两种方案，一是具有信号发生器的强大变送器，二是带有智能手机的便携式变送器；前一种方案成本过于高，本文不做分析，后一种方案的实现成本在我们可接收的范围。<br>但原文中对后一方案的实现没有太多介绍，于是我通过邮件咨询了作者-闫琛博士，闫博士非常友好，我是在晚上十点发送的第一封邮件，差不多在十分钟内通过几封邮件的交流，解决了我的问题，很快确定了我的具体实现路径，非常感谢大佬！</p>
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<ul>
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<li>Q: 使用便携式设备攻击的时候，三星Galaxy S6 Edge发送的高频声音信号是怎么生成的呢？是预先使用专业设备调制好的信号保存为mp3吗？</li>
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<li>A: 通过软件调制，生成.wav的超声波音频文件，再通过三星手机播放的。</li>
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<li>Q: 用的是什么软件进行调制?</li>
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<li>A: 用过matlab和python，都是可以的</li>
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</ul>
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<h2 id="0x01-语音命令生成"><a href="#0x01-语音命令生成" class="headerlink" title="0x01 语音命令生成"></a>0x01 语音命令生成</h2><p><a href="https://ttstool.com/" target="_blank" rel="noopener">https://ttstool.com/</a><br>微软的TTS接口生成的是mp3格式音频，一般来说我们使用python处理音频都是针对wav格式。<br><a href="https://www.aconvert.com/cn/audio/mp3-to-wav/" target="_blank" rel="noopener">https://www.aconvert.com/cn/audio/mp3-to-wav/</a><br>我们可以通过这个网站对格式做转换。<br><a href="https://coolyim.quip.com/-/blob/OVVAAAmjZcr/Eq9qXdQ7_eD5KQaR33wCCw?name=xiaoyi.wav" target="_blank" rel="noopener">xiaoyi.wav</a><br>这个网站的采样率最高只能达到96000hz<br><a href="https://coolyim.quip.com/-/blob/OVVAAAmjZcr/aZfltfEV_ZxV1LCGznB1OA?name=6wxmu-crusr.wav" target="_blank" rel="noopener">6wxmu-crusr.wav</a></p>
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<h2 id="0x02-语音命令调制"><a href="#0x02-语音命令调制" class="headerlink" title="0x02 语音命令调制"></a>0x02 语音命令调制</h2><p>生成语音命令的基带信号后，我们需要在超声载波上对其进行调制，以使它们听不到。 为了利用麦克风的非线性，DolphinAttack必须利用幅度调制（AM）。</p>
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<h3 id="AM调制原理"><a href="#AM调制原理" class="headerlink" title="AM调制原理"></a>AM调制原理</h3><p>使载波的振幅按照所需传送信号的变化规律而变化，但频率保持不变的调制方法。调幅在有线电或无线电通信和广播中应用甚广。调幅是高频载波的振幅随信号改变的调制（AM）。其中，载波信号的振幅随着调制信号的某种特征的变换而变化。例如，0或1分别对应于无载波或有载波输出，电视的图像信号使用调幅。调频的抗干扰能力强，失真小，但服务半径小。<br>假设载波uc(t)和调制信号的频率分别为ωc和Ω，在已调波中包含三个频率成分：ωc、ωc+Ω和ωc-Ω。ωc+Ω称为上边频，ωc-Ω称为下边频。</p>
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<p><a href="https://epxx.co/artigos/ammodulation.html" target="_blank" rel="noopener">https://epxx.co/artigos/ammodulation.html</a><br><a href="http://www.chenjianqu.com/show-44.html" target="_blank" rel="noopener">http://www.chenjianqu.com/show-44.html</a><br><a href="https://zhuanlan.zhihu.com/p/54561504" target="_blank" rel="noopener">https://zhuanlan.zhihu.com/p/54561504</a><br><a href="http://www.mwhitelab.com/archives/208" target="_blank" rel="noopener">http://www.mwhitelab.com/archives/208</a></p>
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<h3 id="使用python调制"><a href="#使用python调制" class="headerlink" title="使用python调制"></a>使用python调制</h3><p>现在我们已经有了基带信号，使用<a href="https://www.fosshub.com/Audacity.html" target="_blank" rel="noopener">Audacity</a>对其进行频谱分析，此语音的带宽或频谱（左图为采样频率48khz音频，右图为96khz） ：<br><img src="https://res.cloudinary.com/dozyfkbg3/image/upload/v1610082052/Dolphin%20Attack/08_YW7UW_PS_TOE_LZZY.png" alt><br><img src="https://res.cloudinary.com/dozyfkbg3/image/upload/v1610082052/Dolphin%20Attack/WGD_U453KYP3M3_2639_5I.png" alt></p>
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<p>我们可以看到带宽为8000-9000hz左右，这是女声，因此频带范围较宽。这可能导致可听范围内的频率泄露，但这里我们先不去讨论，之后再使用带宽较小的语音以创建基带语音信号。<br>wave包最多能读取的wav音频采样率为<a href="https://github.com/jiaaro/pydub/issues/134" target="_blank" rel="noopener">48khz</a>，当超过这个值时，wave就不再支持（wave.Error: unknown format: 65534）。但我们的载波频率为30khz左右，这就要求音频文件的采样率高于60khz才能保证不失真。所幸<a href="https://kite.com/python/docs/scipy.io.wavfile" target="_blank" rel="noopener"><code>scipy.io.wavfile</code></a>支持高于48khz的wav文件读取。<br>使用以下Python程序来生成调制的AM和AM-SC音频，AM是广播无线电调制的“正常”声音，它加上了载波；AM-SC则只是载波与原始信号的乘积。</p>
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<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># coding=utf-8</span></span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">import</span> matplotlib.pyplot <span class="keyword">as</span> plt</span><br><span class="line"><span class="keyword">import</span> os</span><br><span class="line"><span class="keyword">import</span> wave</span><br><span class="line"><span class="keyword">import</span> struct</span><br><span class="line"><span class="keyword">import</span> math</span><br><span class="line"><span class="keyword">from</span> pydub <span class="keyword">import</span> AudioSegment</span><br><span class="line"><span class="keyword">import</span> scipy.io.wavfile</span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">main</span><span class="params">()</span>:</span></span><br><span class="line"> test = scipy.io.wavfile.read(<span class="string">"xiaoyi.wav"</span>)</span><br><span class="line"> nframes = len(test[<span class="number">1</span>])</span><br><span class="line"> waveData = np.fromstring(test[<span class="number">1</span>],dtype=np.short)<span class="comment">#将原始字符数据转换为整数</span></span><br><span class="line"> <span class="comment">#音频数据归一化</span></span><br><span class="line"> maxW = max(abs(waveData))</span><br><span class="line"> waveData = waveData * <span class="number">1.0</span>/maxW</span><br><span class="line"> <span class="comment">#将音频信号规整乘每行一路通道信号的格式，即该矩阵一行为一个通道的采样点，共nchannels行</span></span><br><span class="line"> Tdata = np.reshape(waveData,[nframes,<span class="number">1</span>]).T <span class="comment"># .T 表示转置</span></span><br><span class="line"> am = wave.open(<span class="string">"am.wav"</span>, <span class="string">"w"</span>)</span><br><span class="line"> amsc = wave.open(<span class="string">"amsc.wav"</span>, <span class="string">"w"</span>)</span><br><span class="line"> carrier = wave.open(<span class="string">"carrier3000.wav"</span>, <span class="string">"w"</span>)</span><br><span class="line"> <span class="keyword">for</span> f <span class="keyword">in</span> [am,amsc,carrier]:</span><br><span class="line"> f.setnchannels(<span class="number">1</span>)</span><br><span class="line"> f.setsampwidth(<span class="number">2</span>)</span><br><span class="line"> f.setframerate(<span class="number">96000</span>)</span><br><span class="line"> <span class="keyword">for</span> n <span class="keyword">in</span> range(<span class="number">0</span>, nframes):</span><br><span class="line"> carrier_sample = math.cos(<span class="number">30000.0</span> * (n / <span class="number">96000.0</span>) * math.pi * <span class="number">2</span>)</span><br><span class="line"> signal_am = signal_amsc= waveData[n] * carrier_sample</span><br><span class="line"> signal_am += carrier_sample</span><br><span class="line"> signal_am /= <span class="number">2</span></span><br><span class="line"> am.writeframes(struct.pack(<span class="string">'h'</span>, signal_am * maxW))</span><br><span class="line"> amsc.writeframes(struct.pack(<span class="string">'h'</span>, signal_amsc * maxW))</span><br><span class="line"> carrier.writeframes(struct.pack(<span class="string">'h'</span>, carrier_sample * maxW))</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="keyword">if</span> __name__==<span class="string">'__main__'</span>:</span><br><span class="line"> main()</span><br></pre></td></tr></table></figure>
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<p>分别对am.wav、amsc.wav、carrier3000.wav做频谱分析<br>carrier3000.wav的频谱的为集中在载波频率30khz上的一个脉冲<a href="https://coolyim.quip.com/-/blob/OVVAAAmjZcr/9RE4Z0lCs1WACO75zLTAhA?name=carrier3000.wav" target="_blank" rel="noopener">carrier3000.wav</a><br><img src="https://res.cloudinary.com/dozyfkbg3/image/upload/v1610082051/Dolphin%20Attack/K5447O57_S___A_O_Q3V.png" alt><br>amsc.wav的带宽约为18khz，是原来的两倍，关于f=30khz镜面对称。AM调制会创建原始信号的两个“副本”，一个在21-30kHz频段，另一个在30-39kHz。<br><img src="https://res.cloudinary.com/dozyfkbg3/image/upload/v1610082052/Dolphin%20Attack/OPSXK_21_7R24_I_NIWM0_8.png" alt></p>
|
||
<p>am.wav，在这种调制中，我们可以听到载波，而在AM-SC中则听不到。频谱类似于AM-SC，但在载波频率上还有一个尖锐的“尖峰”：<br><img src="https://res.cloudinary.com/dozyfkbg3/image/upload/v1610082051/Dolphin%20Attack/G8_K4_ZG__PE2CQ_5_UYY.png" alt></p>
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<h2 id="0x03-语音命令发送器"><a href="#0x03-语音命令发送器" class="headerlink" title="0x03 语音命令发送器"></a>0x03 语音命令发送器</h2><p>下图是由智能手机驱动的便携式发射器。便携式发射器利用智能手机来发射调制信号。许多设备的最佳载波频率都大于24 kHz， 大多数智能手机无法完成任务。大多数智能手机最多支持48 kHz采样率，所以只能发送载波频率最高为24 kHz的调制窄带信号。需要支持高达192 kHz的采样率的手机，而且扬声器会衰减频率大于20 kHz的信号。为了减轻这个问题，我使用窄带超声换能器作为扬声器，并在超声换能器之前添加了一个放大器，这样有效的攻击范围得以扩展。</p>
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|
||
<script type="text/javascript" src="/lib/jquery/index.js?v=2.1.3"></script>
|
||
|
||
|
||
|
||
|
||
<script type="text/javascript" src="/lib/fastclick/lib/fastclick.min.js?v=1.0.6"></script>
|
||
|
||
|
||
|
||
|
||
<script type="text/javascript" src="/lib/jquery_lazyload/jquery.lazyload.js?v=1.9.7"></script>
|
||
|
||
|
||
|
||
|
||
<script type="text/javascript" src="/lib/velocity/velocity.min.js?v=1.2.1"></script>
|
||
|
||
|
||
|
||
|
||
<script type="text/javascript" src="/lib/velocity/velocity.ui.min.js?v=1.2.1"></script>
|
||
|
||
|
||
|
||
|
||
<script type="text/javascript" src="/lib/fancybox/source/jquery.fancybox.pack.js?v=2.1.5"></script>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<script type="text/javascript" src="/js/src/utils.js?v=5.1.4"></script>
|
||
|
||
<script type="text/javascript" src="/js/src/motion.js?v=5.1.4"></script>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<script type="text/javascript" src="/js/src/scrollspy.js?v=5.1.4"></script>
|
||
<script type="text/javascript" src="/js/src/post-details.js?v=5.1.4"></script>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<script type="text/javascript" src="/js/src/bootstrap.js?v=5.1.4"></script>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
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||
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||
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||
|
||
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||
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||
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||
|
||
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||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<!-- LOCAL: You can save these files to your site and update links -->
|
||
|
||
<link rel="stylesheet" href="https://unpkg.com/gitalk/dist/gitalk.css">
|
||
<script src="https://unpkg.com/gitalk/dist/gitalk.min.js"></script>
|
||
<!-- END LOCAL -->
|
||
|
||
|
||
<script type="text/javascript">
|
||
function renderGitalk(){
|
||
var gitalk = new Gitalk({
|
||
owner: 'Cool-Y',
|
||
repo: 'gitment-comments',
|
||
clientID: '180955a2c3ae3d966d9a',
|
||
clientSecret: '1c5db4da72df5e6fc318d12afe5f4406f7c54343',
|
||
admin: 'Cool-Y',
|
||
id: decodeURI(location.pathname),
|
||
|
||
distractionFreeMode: 'true'
|
||
|
||
});
|
||
gitalk.render('gitalk-container');
|
||
}
|
||
renderGitalk();
|
||
</script>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<script src="https://cdn1.lncld.net/static/js/av-core-mini-0.6.4.js"></script>
|
||
<script>AV.initialize("EWwoJgHNdlj6iBjiFlMcabUO-gzGzoHsz", "x8FxDrYG79C8YFrTww9ljo8K");</script>
|
||
<script>
|
||
function showTime(Counter) {
|
||
var query = new AV.Query(Counter);
|
||
var entries = [];
|
||
var $visitors = $(".leancloud_visitors");
|
||
|
||
$visitors.each(function () {
|
||
entries.push( $(this).attr("id").trim() );
|
||
});
|
||
|
||
query.containedIn('url', entries);
|
||
query.find()
|
||
.done(function (results) {
|
||
var COUNT_CONTAINER_REF = '.leancloud-visitors-count';
|
||
|
||
if (results.length === 0) {
|
||
$visitors.find(COUNT_CONTAINER_REF).text(0);
|
||
return;
|
||
}
|
||
|
||
for (var i = 0; i < results.length; i++) {
|
||
var item = results[i];
|
||
var url = item.get('url');
|
||
var time = item.get('time');
|
||
var element = document.getElementById(url);
|
||
|
||
$(element).find(COUNT_CONTAINER_REF).text(time);
|
||
}
|
||
for(var i = 0; i < entries.length; i++) {
|
||
var url = entries[i];
|
||
var element = document.getElementById(url);
|
||
var countSpan = $(element).find(COUNT_CONTAINER_REF);
|
||
if( countSpan.text() == '') {
|
||
countSpan.text(0);
|
||
}
|
||
}
|
||
})
|
||
.fail(function (object, error) {
|
||
console.log("Error: " + error.code + " " + error.message);
|
||
});
|
||
}
|
||
|
||
function addCount(Counter) {
|
||
var $visitors = $(".leancloud_visitors");
|
||
var url = $visitors.attr('id').trim();
|
||
var title = $visitors.attr('data-flag-title').trim();
|
||
var query = new AV.Query(Counter);
|
||
|
||
query.equalTo("url", url);
|
||
query.find({
|
||
success: function(results) {
|
||
if (results.length > 0) {
|
||
var counter = results[0];
|
||
counter.fetchWhenSave(true);
|
||
counter.increment("time");
|
||
counter.save(null, {
|
||
success: function(counter) {
|
||
var $element = $(document.getElementById(url));
|
||
$element.find('.leancloud-visitors-count').text(counter.get('time'));
|
||
},
|
||
error: function(counter, error) {
|
||
console.log('Failed to save Visitor num, with error message: ' + error.message);
|
||
}
|
||
});
|
||
} else {
|
||
var newcounter = new Counter();
|
||
/* Set ACL */
|
||
var acl = new AV.ACL();
|
||
acl.setPublicReadAccess(true);
|
||
acl.setPublicWriteAccess(true);
|
||
newcounter.setACL(acl);
|
||
/* End Set ACL */
|
||
newcounter.set("title", title);
|
||
newcounter.set("url", url);
|
||
newcounter.set("time", 1);
|
||
newcounter.save(null, {
|
||
success: function(newcounter) {
|
||
var $element = $(document.getElementById(url));
|
||
$element.find('.leancloud-visitors-count').text(newcounter.get('time'));
|
||
},
|
||
error: function(newcounter, error) {
|
||
console.log('Failed to create');
|
||
}
|
||
});
|
||
}
|
||
},
|
||
error: function(error) {
|
||
console.log('Error:' + error.code + " " + error.message);
|
||
}
|
||
});
|
||
}
|
||
|
||
$(function() {
|
||
var Counter = AV.Object.extend("Counter");
|
||
if ($('.leancloud_visitors').length == 1) {
|
||
addCount(Counter);
|
||
} else if ($('.post-title-link').length > 1) {
|
||
showTime(Counter);
|
||
}
|
||
});
|
||
</script>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<script>
|
||
(function(){
|
||
var bp = document.createElement('script');
|
||
var curProtocol = window.location.protocol.split(':')[0];
|
||
if (curProtocol === 'https') {
|
||
bp.src = 'https://zz.bdstatic.com/linksubmit/push.js';
|
||
}
|
||
else {
|
||
bp.src = 'http://push.zhanzhang.baidu.com/push.js';
|
||
}
|
||
var s = document.getElementsByTagName("script")[0];
|
||
s.parentNode.insertBefore(bp, s);
|
||
})();
|
||
</script>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
</body>
|
||
</html>
|