使用Julia语言破解滑块验证码的完整流程解析
在本篇文章中，我们将使用 Julia 语言来破解滑块验证码。我们将详细介绍如何通过下载滑块验证码图片、计算滑块的移动距离、生成滑动轨迹，以及最后发送验证请求。
- 下载验证码图片
首先，我们需要下载滑块验证码的前景和背景图片。在 Julia 中，我们可以使用 HTTP.jl 包来发送 HTTP 请求并下载图片。
julia
using HTTP
using Base64
function download_image(url::String, filepath::String)
response = HTTP.get(url)
if response.status == 200
open(filepath, "w") do f更多内容联系1436423940
write(f, response.body)
end
println("Downloaded image from $url")
else
error("Failed to download image from $url")
end
end
download_image("https://captcha.com/bg.png", "background.png")
download_image("https://captcha.com/fg.png", "foreground.png")
2. 计算滑块移动距离
接下来，我们需要计算滑块的移动距离。我们将对比前景图和背景图的像素差异来确定滑块应该移动的距离。这里我们会使用 Images.jl 处理图片数据。
julia
using Images
function calculate_distance(bg_path::String, fg_path::String)
bg_img = load(bg_path)
fg_img = load(fg_path)
for x in 1:size(bg_img, 2) # 遍历图片的宽度
for y in 1:size(bg_img, 1) # 遍历图片的高度
if bg_img[y, x] != fg_img[y, x]
println("Slide distance: $x")
return x # 返回滑动距离
end
end
end
error("No difference found between images")
end
distance = calculate_distance("background.png", "foreground.png")
println("Calculated slide distance: $distance")
3. 生成滑动轨迹
我们需要生成一个模拟的滑动轨迹，其中包括每一步的坐标和时间延迟。滑动轨迹会通过随机生成每次的移动距离和时间间隔。
julia
function generate_track(distance::Int)
track = []
current_position = 0
random_delay = () -> rand(10:30) # 随机生成10-30ms的延迟
while current_position < distance
move_step = rand(1:3) # 每次移动1-3个像素
current_position += move_step
if current_position > distance
current_position = distance
end
push!(track, (current_position, 0, random_delay())) # (x, y, delay)
end
return track
end
track = generate_track(distance)
println("Generated track: $track")
4. 发送验证请求
最后，我们需要将滑动的距离和生成的滑动轨迹发送到服务端进行验证。在 Julia 中，我们可以使用 HTTP.jl 包发送 POST 请求。
julia
function send_verification(distance::Int, track::Array{Tuple{Int, Int, Int}, 1})
track_data = "[" * join(map(x -> "[$(x[1]), $(x[2]), $(x[3])]", track), ",") * "]"
body = "distance=$distance&track=$track_data"
response = HTTP.post("https://captcha.com/verify",
["Content-Type" => "application/x-www-form-urlencoded"],
body)
println("Verification response: $(response.status)")
end
send_verification(distance, track)
5. 主程序整合
最后，我们将所有步骤整合在一起形成一个完整的破解流程。
julia
using HTTP
using Images
下载图片
function download_image(url::String, filepath::String)
response = HTTP.get(url)
if response.status == 200
open(filepath, "w") do f
write(f, response.body)
end
println("Downloaded image from $url")
else
error("Failed to download image from $url")
end
end
计算滑动距离
function calculate_distance(bg_path::String, fg_path::String)
bg_img = load(bg_path)
fg_img = load(fg_path)
for x in 1:size(bg_img, 2)
for y in 1:size(bg_img, 1)
if bg_img[y, x] != fg_img[y, x]
return x
end
end
end
error("No difference found between images")
end
生成滑动轨迹
function generate_track(distance::Int)
track = []
current_position = 0
random_delay = () -> rand(10:30)
while current_position < distance
move_step = rand(1:3)
current_position += move_step
if current_position > distance
current_position = distance
end
push!(track, (current_position, 0, random_delay()))
end
return track
end
发送验证请求
function send_verification(distance::Int, track::Array{Tuple{Int, Int, Int}, 1})
track_data = "[" * join(map(x -> "[$(x[1]), $(x[2]), $(x[3])]", track), ",") * "]"
body = "distance=$distance&track=$track_data"
response = HTTP.post("https://captcha.com/verify",
["Content-Type" => "application/x-www-form-urlencoded"],
body)
println("Verification response: $(response.status)")
end
主函数
function main()
# 1. 下载验证码图片
download_image("https://captcha.com/bg.png", "background.png")
download_image("https://captcha.com/fg.png", "foreground.png")
# 2. 计算滑动距离
distance = calculate_distance("background.png", "foreground.png")
# 3. 生成滑动轨迹
track = generate_track(distance)
# 4. 发送验证请求
send_verification(distance, track)
end
main()
