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| import gradio as gr | |
| import cv2 | |
| import io | |
| import pandas as pd | |
| from LSBSteg import LSBSteg | |
| def convert(file): | |
| print(f"Converting file {file}") | |
| in_img = cv2.imread(file, cv2.IMREAD_UNCHANGED) | |
| lsbsteg = LSBSteg(in_img) | |
| data = lsbsteg.decode_binary() | |
| bytes = io.BytesIO(data) | |
| dataframe = pd.read_parquet(bytes) | |
| # dataframe.to_csv('output.csv') | |
| return dataframe | |
| with gr.Blocks() as demo: | |
| gr.Markdown(""" | |
| ## Non-Suspicious image decoder | |
| This tool shows the extraction a dataframe hidden inside an image. | |
| There are a few ways to hide data into a PNG file, notably: | |
| * adding it after the end of the file (after the PNG IEND chunk), so that it gets | |
| ignored by image viewers | |
| * adding it as comments in the PNG file (tEXt chunks) | |
| These methods are kind of easy to spot! Also, a lot of software, browsers, image upload | |
| websites etc often just strip them. | |
| So, here, we have a different, more thoughtful (and arguably cooler) method. | |
| This class hides the data using a basic kind of **[steganography](https://en.wikipedia.org/wiki/Steganography)**: | |
| it hides it in the | |
| *least significant bits* of the raw (uncompressed) picture: tiny differences in the red, green and blue | |
| channel of the image encodes the data we're interested in. | |
| This means the resulting picture | |
| looks **very close to the original image**; and for the data we hide here, it is **inperceptible | |
| to the naked eye**. | |
| The resulting PNG file will probably get a little bit bigger as a result, since PNG uses compression, | |
| which will have a harder time when we have our stolen data injected into the image. This is | |
| not that much of a problem since it stays <100Ko, so it's not that noticeable. | |
| """) | |
| with gr.Row(): | |
| im = gr.Image(label="Input image file", type="filepath") | |
| def preprocess(encoding: str) -> str: | |
| # We do our own preprocessing because gradio's deletes PNG metadata :( | |
| import tempfile | |
| import base64 | |
| content = encoding.split(";")[1] | |
| image_encoded = content.split(",")[1] | |
| png_content = base64.b64decode(image_encoded) | |
| file_obj = tempfile.NamedTemporaryFile( | |
| delete=False, | |
| suffix=".input.png", | |
| ) | |
| file_obj.write(png_content) | |
| return file_obj.name | |
| im.preprocess = preprocess | |
| df_out = gr.Dataframe( | |
| label="Output dataframe", max_rows=20, overflow_row_behaviour="paginate" | |
| ) | |
| # file_out = gr.File(label="Full output CSV file") | |
| btn = gr.Button(value="Extract") | |
| gr.Markdown("Click on the example below to get the data from the associated colab notebook :)") | |
| gr.Examples( | |
| examples=["sample-picture.png"], | |
| inputs=[im], | |
| outputs=[df_out], | |
| fn=convert, | |
| cache_examples=True, | |
| ) | |
| # demo = gr.Interface(convert, im, im_2) | |
| btn.click(convert, inputs=[im], outputs=[df_out]) | |
| # example_img = os.path.join(os.path.dirname(__file__), "example-picture.png") | |
| if __name__ == "__main__": | |
| demo.launch() | |